Medina_Unsupervised_Learning_Project.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tIOusyeT4_RJ"
   },
   "source": [
    "## Project: Unsupervised Learning\n",
    "----------------------------------------\n",
    "-----------------------------------------\n",
    "\n",
    "We will be using the Credit Card Customer Data for this project.\n",
    "\n",
    "----------------------------\n",
    "## Context: \n",
    "-----------------------------\n",
    "AllLife Bank wants to focus on its credit card customer base in the next financial year. They have been advised by their marketing research team, that the penetration in the market can be improved. Based on this input, the Marketing team proposes to run personalized campaigns to target new customers as well as upsell to existing customers. Another insight from the market research was that the customers perceive the support services of the back poorly. Based on this, the Operations team wants to upgrade the service delivery model, to ensure that customers queries are resolved faster. Head of Marketing and Head of Delivery both decide to reach out to the Data Science team for help.\n",
    "\n",
    "\n",
    "----------------------------\n",
    "## Objective: \n",
    "-----------------------------\n",
    "\n",
    "Identify different segments in the existing customer based on their spending patterns as well as past interaction with the bank.\n",
    "\n",
    "--------------------------\n",
    "## About the data:\n",
    "--------------------------\n",
    "Data is of various customers of a bank with their credit limit, the total number of credit cards the customer has, and different channels through which customer has contacted the bank for any queries, different channels include visiting the bank, online and through a call centre.\n",
    "\n",
    "- Sl_no - Customer Serial Number\n",
    "- Customer Key - Customer identification\n",
    "- Avg_Credit_Limit\t- Average credit limit (currency is not specified, you can make an assumption around this)\n",
    "- Total_Credit_Cards\t- Total number of credit cards \n",
    "- Total_visits_bank\t- Total bank visits\n",
    "- Total_visits_online -\t Total online visits\n",
    "- Total_calls_made - Total calls made"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ReVFSgtvvO6"
   },
   "source": [
    "## Importing libraries and overview of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "KJKn2dkPKnkU"
   },
   "outputs": [],
   "source": [
    "#Import all the necessary packages\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "#to scale the data using z-score \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#importing clustering algorithms\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "\n",
    "#if the below line of code gives an error, then uncomment the following code to install the sklearn_extra library\n",
    "#!pip install scikit-learn-extra\n",
    "from sklearn_extra.cluster import KMedoids\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9qEgcn2XvvO8"
   },
   "source": [
    "#### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "id": "p4QVUUO3VHHH",
    "outputId": "6bd7504e-4281-41ca-f508-5a61927e94b1"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sl_No</th>\n",
       "      <th>Customer Key</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>1</td>\n",
       "      <td>87073</td>\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>2</td>\n",
       "      <td>38414</td>\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>3</td>\n",
       "      <td>17341</td>\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>4</td>\n",
       "      <td>40496</td>\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>5</td>\n",
       "      <td>47437</td>\n",
       "      <td>100000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sl_No  Customer Key  Avg_Credit_Limit  Total_Credit_Cards  \\\n",
       "0      1         87073            100000                   2   \n",
       "1      2         38414             50000                   3   \n",
       "2      3         17341             50000                   7   \n",
       "3      4         40496             30000                   5   \n",
       "4      5         47437            100000                   6   \n",
       "\n",
       "   Total_visits_bank  Total_visits_online  Total_calls_made  \n",
       "0                  1                    1                 0  \n",
       "1                  0                   10                 9  \n",
       "2                  1                    3                 4  \n",
       "3                  1                    1                 4  \n",
       "4                  0                   12                 3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('Credit Card Customer Data.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZmxlhPKovvO-"
   },
   "source": [
    "#### Check the info of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "dtroAFl9vvO-",
    "outputId": "bff20f83-1d85-488e-e1dc-e73c56f563a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 660 entries, 0 to 659\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count  Dtype\n",
      "---  ------               --------------  -----\n",
      " 0   Sl_No                660 non-null    int64\n",
      " 1   Customer Key         660 non-null    int64\n",
      " 2   Avg_Credit_Limit     660 non-null    int64\n",
      " 3   Total_Credit_Cards   660 non-null    int64\n",
      " 4   Total_visits_bank    660 non-null    int64\n",
      " 5   Total_visits_online  660 non-null    int64\n",
      " 6   Total_calls_made     660 non-null    int64\n",
      "dtypes: int64(7)\n",
      "memory usage: 36.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EPP73jY5vvPA"
   },
   "source": [
    "**Observations:**\n",
    "\n",
    "- There are 660 observations and 7 columns in the dataset.\n",
    "- All columns have 660 non-null values i.e. there are no missing values.\n",
    "- All columns are of int64 data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DOP-MnWovvPA"
   },
   "source": [
    "**There are no missing values. Let us now figure out the uniques in each column.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "OCRC8zfDvvPB",
    "outputId": "ad647d25-ef4e-48fa-9f7e-6f1916668f0e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sl_No                  660\n",
       "Customer Key           655\n",
       "Avg_Credit_Limit       110\n",
       "Total_Credit_Cards      10\n",
       "Total_visits_bank        6\n",
       "Total_visits_online     16\n",
       "Total_calls_made        11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JjjUYpvovvPC"
   },
   "source": [
    "- Customer key, which is an identifier, has repeated values. We should treat the same accordingly before applying any algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fpZolGOdvvPC"
   },
   "source": [
    "## Data Preprocessing and Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "bWj8abFqvvPD"
   },
   "outputs": [],
   "source": [
    "# Identify the duplicated customer keys\n",
    "duplicate_keys = data.duplicated(subset=['Customer Key'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "WeUihO0KvvPF"
   },
   "outputs": [],
   "source": [
    "# Drop duplicated keys\n",
    "data = data.drop_duplicates(subset=['Customer Key'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IDaRoTAQvvPF"
   },
   "source": [
    "We have done some basic checks. Now, let's drop the variables that are not required for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "albq4GJ2vvPF"
   },
   "outputs": [],
   "source": [
    "data.drop(columns = ['Sl_No', 'Customer Key'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fRqvXDr4vvPG"
   },
   "source": [
    "Now that we have dropped unnecessary column. We can again check for duplicates. Duplicates would mean customers with identical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "J2EV_kXUvvPG",
    "outputId": "c4159aa4-6b34-4677-b600-063d6e13c529"
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>162</th>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9000</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>18000</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>12000</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>8000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>5000</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>47000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>52000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "162              8000                   2                  0   \n",
       "175              6000                   1                  0   \n",
       "215              8000                   4                  0   \n",
       "295             10000                   6                  4   \n",
       "324              9000                   4                  5   \n",
       "361             18000                   6                  3   \n",
       "378             12000                   6                  5   \n",
       "385              8000                   7                  4   \n",
       "395              5000                   4                  5   \n",
       "455             47000                   6                  2   \n",
       "497             52000                   4                  2   \n",
       "\n",
       "     Total_visits_online  Total_calls_made  \n",
       "162                    3                 4  \n",
       "175                    2                 5  \n",
       "215                    4                 7  \n",
       "295                    2                 3  \n",
       "324                    0                 4  \n",
       "361                    1                 4  \n",
       "378                    2                 1  \n",
       "385                    2                 0  \n",
       "395                    0                 1  \n",
       "455                    0                 4  \n",
       "497                    1                 2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V7Xt_Ss3vvPG"
   },
   "source": [
    "We can drop these duplicated rows from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "aBnoG0IivvPH"
   },
   "outputs": [],
   "source": [
    "data=data[~data.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Bgj7fBervvPH",
    "outputId": "7181e588-68b1-4184-ed6c-687d1997dc47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(644, 5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JtAIAYr9vvPH"
   },
   "source": [
    "- After removing duplicated keys and rows and unnecessary columns, there are 644 unique observations and 5 columns in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LmykagZ0vvPI"
   },
   "source": [
    "#### Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "yg-YXbPNvvPI",
    "outputId": "4c9d30bf-f6d2-4922-d340-7db9b8f6ce24"
   },
   "outputs": [
    {
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       "      <th>Avg_Credit_Limit</th>\n",
       "      <td>644.0</td>\n",
       "      <td>34543.478261</td>\n",
       "      <td>37428.704286</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>48000.00</td>\n",
       "      <td>200000.0</td>\n",
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       "      <th>Total_Credit_Cards</th>\n",
       "      <td>644.0</td>\n",
       "      <td>4.694099</td>\n",
       "      <td>2.175338</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>6.00</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>2.395963</td>\n",
       "      <td>1.626964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.624224</td>\n",
       "      <td>2.957728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>15.0</td>\n",
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       "    <tr>\n",
       "      <th>Total_calls_made</th>\n",
       "      <td>644.0</td>\n",
       "      <td>3.608696</td>\n",
       "      <td>2.880025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean           std     min      25%  \\\n",
       "Avg_Credit_Limit     644.0  34543.478261  37428.704286  3000.0  11000.0   \n",
       "Total_Credit_Cards   644.0      4.694099      2.175338     1.0      3.0   \n",
       "Total_visits_bank    644.0      2.395963      1.626964     0.0      1.0   \n",
       "Total_visits_online  644.0      2.624224      2.957728     0.0      1.0   \n",
       "Total_calls_made     644.0      3.608696      2.880025     0.0      1.0   \n",
       "\n",
       "                         50%       75%       max  \n",
       "Avg_Credit_Limit     18000.0  48000.00  200000.0  \n",
       "Total_Credit_Cards       5.0      6.00      10.0  \n",
       "Total_visits_bank        2.0      4.00       5.0  \n",
       "Total_visits_online      2.0      4.00      15.0  \n",
       "Total_calls_made         3.0      5.25      10.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kAEzS8-GvvPI"
   },
   "source": [
    "**Observations:___________**\n",
    "\n",
    "- All features have 644 values - which is good because we don't have to worry about i mputing missing values\n",
    "- Avg_Credit_limit has a large mean (34K) and a very large standard deviation (37K)\n",
    "- Total Credit Card average is 4.69 with a std deviation of 2.17 - the most typical credit card count (50% percentile) is 5\n",
    "- Total Bank Visits has a mean of 2.39 and std dev 1.62 - which means that some people never visit the bank (min of 0)\n",
    "- Total Visits Online has a mean of 2.6 and std dev of 2.9 - which means that some people never visit the website (min of 0)\n",
    "- Lasttly, total calls has a mean of 3.6 and std dev of 2.9 - with 50% showing = 3, calls are fairly prevalant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xUxHbmglvvPJ"
   },
   "source": [
    "#### Now let's go ahead with the exploring each variable at hand. We will check the distribution and outliers for each variable in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "TmKqRM1JvvPJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg_Credit_Limit\n",
      "Skew : 2.19\n",
      "Mean: 34543.48 | Median: 18000.0 | 2nd Qrt: 11000.0 | 3rd Qrt: 48000.0\n",
      "p-value: 0.0, stat: 0.725 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_Credit_Cards\n",
      "Skew : 0.17\n",
      "Mean: 4.69 | Median: 5.0 | 2nd Qrt: 3.0 | 3rd Qrt: 6.0\n",
      "p-value: 0.0, stat: 0.956 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_visits_bank\n",
      "Skew : 0.15\n",
      "Mean: 2.4 | Median: 2.0 | 2nd Qrt: 1.0 | 3rd Qrt: 4.0\n",
      "p-value: 0.0, stat: 0.916 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_visits_online\n",
      "Skew : 2.21\n",
      "Mean: 2.62 | Median: 2.0 | 2nd Qrt: 1.0 | 3rd Qrt: 4.0\n",
      "p-value: 0.0, stat: 0.748 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total_calls_made\n",
      "Skew : 0.65\n",
      "Mean: 3.61 | Median: 3.0 | 2nd Qrt: 1.0 | 3rd Qrt: 5.25\n",
      "p-value: 0.0, stat: 0.916 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uncomment and complete the code by filling the blanks \n",
    "\n",
    "def fn_guassian(dataIN):\n",
    "    \n",
    "    stat, p = shapiro(dataIN)\n",
    "    stat = round(stat,3)\n",
    "    p = round(p,3)\n",
    "    normtxt = 'probably NOT Gaussian'\n",
    "    if p > .05:\n",
    "        normtxt = 'probably Gaussian'\n",
    "    print(f'p-value: {p}, stat: {stat} and {normtxt} ')\n",
    "\n",
    "\n",
    "for col in data.columns:\n",
    "    print(col)\n",
    "    print('Skew :',round(data[col].skew(),2))\n",
    "    plt.figure(figsize=(15,4))\n",
    "    plt.subplot(1,2,1)\n",
    "    data[col].hist()\n",
    "    print(f'Mean: {round(data[col].mean(),2)} | Median: {data[col].median()} | 2nd Qrt: {data[col].quantile(0.25)} | 3rd Qrt: {data[col].quantile(0.75)}')\n",
    "    fn_guassian(data[col])\n",
    "    plt.ylabel('count')\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.boxplot(x=data[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yuraaJ3wvvPK"
   },
   "source": [
    "**Observation:______________**\n",
    "\n",
    "- none of the distributions appear to be gaussian (confirmed by shapiro testing)\n",
    "- Avg credit limit and Total visits online are the only data points with outliers - we'll have to be careful on how we treat these variables in our analysis so as not to skew our results unintentionally\n",
    "- Avg credit limit skewed left - with a mean of 35K and median of 18K. The IQR is 11K to 48K\n",
    "- Total credit cards has a mean of 4.6 and median of 5 - with an IQR of 3 to 6\n",
    "- Total visits to bank has a mean of 2.4 and median of 2 - with an IQR of 1 to 4. \n",
    "- Total Calls Made is skewed slightly left - with a mean of 3.6 and median of 3 - with an IQR of 1 to 5.25. There's quite a bit of spread in this data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KGeGbF8ovvPK"
   },
   "source": [
    "**Now, let's check the correlation among different variables.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "-INtorZDvvPK",
    "outputId": "d00ff8b6-82f7-4366-8da6-f61d0e6aabe4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(data.corr(), annot=True, fmt='0.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_rQynXTvvPL"
   },
   "source": [
    "**Observation:**\n",
    "\n",
    "- Avg_Credit_Limit is positively correlated with Total_Credit_Cards Total_visits_online which can makes sense.\n",
    "- Avg_Credit_Limit is negatively correlated with Total_calls_made and Total_visits_bank.\n",
    "- Total_visits_bank, Total_visits_online, Total_calls_made are negatively correlated which implies that majority of customers use only one of these channels to contact the bank."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XFimA8PUvvPL"
   },
   "source": [
    "#### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "TjqSX-50vvPL"
   },
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "data_scaled=pd.DataFrame(scaler.fit_transform(data), columns=data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "L2cyzdZNvvPM",
    "outputId": "42074a5e-e393-4450-9f1f-75d9be79f194"
   },
   "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>1.750192</td>\n",
       "      <td>-1.239437</td>\n",
       "      <td>-0.858684</td>\n",
       "      <td>-0.549573</td>\n",
       "      <td>-1.253982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.413280</td>\n",
       "      <td>-0.779381</td>\n",
       "      <td>-1.473803</td>\n",
       "      <td>2.495669</td>\n",
       "      <td>1.873420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.413280</td>\n",
       "      <td>1.060843</td>\n",
       "      <td>-0.858684</td>\n",
       "      <td>0.127148</td>\n",
       "      <td>0.135974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.121485</td>\n",
       "      <td>0.140731</td>\n",
       "      <td>-0.858684</td>\n",
       "      <td>-0.549573</td>\n",
       "      <td>0.135974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.750192</td>\n",
       "      <td>0.600787</td>\n",
       "      <td>-1.473803</td>\n",
       "      <td>3.172390</td>\n",
       "      <td>-0.211515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0          1.750192           -1.239437          -0.858684   \n",
       "1          0.413280           -0.779381          -1.473803   \n",
       "2          0.413280            1.060843          -0.858684   \n",
       "3         -0.121485            0.140731          -0.858684   \n",
       "4          1.750192            0.600787          -1.473803   \n",
       "\n",
       "   Total_visits_online  Total_calls_made  \n",
       "0            -0.549573         -1.253982  \n",
       "1             2.495669          1.873420  \n",
       "2             0.127148          0.135974  \n",
       "3            -0.549573          0.135974  \n",
       "4             3.172390         -0.211515  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "3ESvHP6SvvPM"
   },
   "outputs": [],
   "source": [
    "#Creating copy of the data to store labels from each algorithm\n",
    "data_scaled_copy = data_scaled.copy(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1lVGJ-HRvvPN"
   },
   "source": [
    "## K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JCT1SB7_vvPN"
   },
   "source": [
    "Let us now fit k-means algorithm on our scaled data and find out the optimum number of clusters to use.\n",
    "\n",
    "We will do this in 3 steps:\n",
    "1. Initialize a dictionary to store the SSE for each k\n",
    "2. Run for a range of Ks and store SSE for each run\n",
    "3. Plot the SSE vs K and find the elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "HkQWkpSovvPN",
    "outputId": "7819694e-9ce6-4ba2-f0bd-8ccd411cb018"
   },
   "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": [
    "# step 1\n",
    "sse = {} \n",
    "\n",
    "# step 2 - iterate for a range of Ks and fit the scaled data to the algorithm. Use inertia attribute from the clustering object and \n",
    "# store the inertia value for that k \n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k, max_iter=1000, random_state=1).fit(data_scaled)\n",
    "    sse[k] = kmeans.inertia_\n",
    "\n",
    "# step 3\n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()), 'bx-')\n",
    "plt.xlabel(\"Number of cluster\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wYIbRxO2vvPO"
   },
   "source": [
    "- Looking at the plot, we can say that elbow point is achieved for k=3.\n",
    "- We will fit the k-means again with k=3 to get the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "bkeg6XsBvvPO"
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=1) #Apply the K-Means algorithm\n",
    "kmeans.fit(data_scaled) #Fit the kmeans function on the scaled data\n",
    "\n",
    "#Adding predicted labels to the original data and scaled data \n",
    "data_scaled_copy['Labels'] = kmeans.predict(data_scaled) #Save the predictions on the scaled data from K-Means\n",
    "data['Labels'] = kmeans.predict(data_scaled) #Save the predictions on the scaled data from K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations for Question 4 ______________**\n",
    "\n",
    "> ### Reason for selecting k=3 - Jose Medina answer:\n",
    "> - According to the plot, after K=3, the Within-Cluster-Sum of Squared Errors starts to diminish\n",
    "\n",
    "--------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9SPMPRF1vvPP"
   },
   "source": [
    "We have generated the labels with k-means. Let us look at the various features based on the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "DDqfjsfl2lew",
    "outputId": "a60da3e1-8312-4b0c-a0ab-54b41b8d86a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    374\n",
       "0    221\n",
       "2     49\n",
       "Name: Labels, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of observations in each cluster\n",
    "data.Labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "eu7EO0nn2lex",
    "outputId": "0716f626-abf8-435a-878d-f2bd73409fd9"
   },
   "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>group_0 Mean</th>\n",
       "      <th>group_1 Mean</th>\n",
       "      <th>group_2 Mean</th>\n",
       "      <th>group_0 Median</th>\n",
       "      <th>group_1 Median</th>\n",
       "      <th>group_2 Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg_Credit_Limit</th>\n",
       "      <td>12239.819005</td>\n",
       "      <td>33893.048128</td>\n",
       "      <td>140102.040816</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>31500.0</td>\n",
       "      <td>145000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <td>2.411765</td>\n",
       "      <td>5.508021</td>\n",
       "      <td>8.775510</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <td>0.945701</td>\n",
       "      <td>3.489305</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_online</th>\n",
       "      <td>3.561086</td>\n",
       "      <td>0.975936</td>\n",
       "      <td>10.979592</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_calls_made</th>\n",
       "      <td>6.891403</td>\n",
       "      <td>1.997326</td>\n",
       "      <td>1.102041</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     group_0 Mean  group_1 Mean   group_2 Mean  \\\n",
       "Avg_Credit_Limit     12239.819005  33893.048128  140102.040816   \n",
       "Total_Credit_Cards       2.411765      5.508021       8.775510   \n",
       "Total_visits_bank        0.945701      3.489305       0.591837   \n",
       "Total_visits_online      3.561086      0.975936      10.979592   \n",
       "Total_calls_made         6.891403      1.997326       1.102041   \n",
       "\n",
       "                     group_0 Median  group_1 Median  group_2 Median  \n",
       "Avg_Credit_Limit            12000.0         31500.0        145000.0  \n",
       "Total_Credit_Cards              2.0             6.0             9.0  \n",
       "Total_visits_bank               1.0             3.0             1.0  \n",
       "Total_visits_online             4.0             1.0            11.0  \n",
       "Total_calls_made                7.0             2.0             1.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating summary statistics of the original data for each label\n",
    "mean = data.groupby('Labels').mean()\n",
    "median = data.groupby('Labels').median()\n",
    "df_kmeans = pd.concat([mean, median], axis=0)\n",
    "df_kmeans.index = ['group_0 Mean', 'group_1 Mean', 'group_2 Mean', 'group_0 Median', 'group_1 Median', 'group_2 Median']\n",
    "df_kmeans.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "bTnGIXvWvvPQ",
    "outputId": "fb854446-d8c0-4254-8b7c-d82ca540226b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing different features w.r.t K-means labels\n",
    "data_scaled_copy.boxplot(by = 'Labels', layout = (1,5),figsize=(20,7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nadx5OyGvvPR"
   },
   "source": [
    "**Cluster Profiles:_______________**\n",
    "\n",
    "> First, it should be noted that because underlying data is non-normal (as shown earlier), using k-means is probably not the best approach as distributions are skewed and means are not representative of underlying distributions. Using medians would be better because it ensures we're not biasing conclusions with the outliers present in a few features. \n",
    "\n",
    "> Cluster 2 has only 49 observations, which makes sense in a way because they have the highest median Avg_Credit_Limit (probably wealthier). These higher Avg_Credit_Limit individuals visits online far more frequently (highest Total_visits_online median) than other clusters, and also have higher median Total_Credit_Cards than other clusters.\n",
    "\n",
    "> The other two clusters are a bit more mixed\n",
    "  > - Cluster 0, has the largest count of observations (374), and has the lowest median Avg_Credit_Limit, lowest median Total_Credit_Cards count, and tend to place more Total_Calls_made (higher median calls) than other clusters (perhaps because of more strained financial circumstances)\n",
    "  \n",
    "  > - Lastly, Cluster 1, has a total count of observations of 221 - they have the highest IQR of Total_Visits_bank and lowest Total_visits_online. But otherwise, are middle of the road between cluster 0 and 2 in terms of Avg_Credit_Limit, Total_Credit_Cards, Total_Calls_made"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B02j8pGUvvPS"
   },
   "source": [
    "## Gaussian Mixture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qYBGfwaVvvPS"
   },
   "source": [
    "Let's create clusters using Gaussian Mixture Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "fBHzul_PvvPS"
   },
   "outputs": [],
   "source": [
    "gmm = GaussianMixture(n_components = 3,random_state = 1) #Apply the Gaussian Mixture algorithm\n",
    "gmm.fit(data_scaled) #Fit the gmm function on the scaled data\n",
    "\n",
    "data_scaled_copy['GmmLabels'] = gmm.predict(data_scaled)\n",
    "data['GmmLabels'] = gmm.predict(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "vBtCZOQB2ley",
    "outputId": "aa7194db-946b-48d4-9717-4eca8dff43eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    374\n",
       "0    221\n",
       "2     49\n",
       "Name: GmmLabels, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of observations in each cluster\n",
    "data.GmmLabels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "m21wTqIgvvPT",
    "outputId": "ae29c6fb-918e-42cb-a6d8-4555f3e69df4"
   },
   "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>group_0 Mean</th>\n",
       "      <th>group_1 Mean</th>\n",
       "      <th>group_2 Mean</th>\n",
       "      <th>group_0 Median</th>\n",
       "      <th>group_1 Median</th>\n",
       "      <th>group_2 Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg_Credit_Limit</th>\n",
       "      <td>12239.819005</td>\n",
       "      <td>33893.048128</td>\n",
       "      <td>140102.040816</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>31500.0</td>\n",
       "      <td>145000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <td>2.411765</td>\n",
       "      <td>5.508021</td>\n",
       "      <td>8.775510</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <td>0.945701</td>\n",
       "      <td>3.489305</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_online</th>\n",
       "      <td>3.561086</td>\n",
       "      <td>0.975936</td>\n",
       "      <td>10.979592</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_calls_made</th>\n",
       "      <td>6.891403</td>\n",
       "      <td>1.997326</td>\n",
       "      <td>1.102041</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     group_0 Mean  group_1 Mean   group_2 Mean  \\\n",
       "Avg_Credit_Limit     12239.819005  33893.048128  140102.040816   \n",
       "Total_Credit_Cards       2.411765      5.508021       8.775510   \n",
       "Total_visits_bank        0.945701      3.489305       0.591837   \n",
       "Total_visits_online      3.561086      0.975936      10.979592   \n",
       "Total_calls_made         6.891403      1.997326       1.102041   \n",
       "\n",
       "                     group_0 Median  group_1 Median  group_2 Median  \n",
       "Avg_Credit_Limit            12000.0         31500.0        145000.0  \n",
       "Total_Credit_Cards              2.0             6.0             9.0  \n",
       "Total_visits_bank               1.0             3.0             1.0  \n",
       "Total_visits_online             4.0             1.0            11.0  \n",
       "Total_calls_made                7.0             2.0             1.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating summary statistics of the original data for each label\n",
    "original_features = [\"Avg_Credit_Limit\",\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\"]\n",
    "\n",
    "mean = data.groupby('GmmLabels').mean()\n",
    "median = data.groupby('GmmLabels').median()\n",
    "df_gmm = pd.concat([mean, median], axis=0)\n",
    "df_gmm.index = ['group_0 Mean', 'group_1 Mean', 'group_2 Mean', 'group_0 Median', 'group_1 Median', 'group_2 Median']\n",
    "df_gmm[original_features].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "CuJf3MeOvvPT",
    "outputId": "7dee619e-e69b-4586-82ab-f6635fda21a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting boxplots with the new GMM based labels\n",
    "\n",
    "features_with_lables = [\"Avg_Credit_Limit\",\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\",\"GmmLabels\"]\n",
    "\n",
    "data_scaled_copy[features_with_lables].boxplot(by = 'GmmLabels', layout = (1,5),figsize=(20,7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Jose Medina - custom analysis shows both plot side by side\n",
    "\n",
    "features_with_lables = [\"Avg_Credit_Limit\",\t\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\",\"GmmLabels\"]\n",
    "df_gmm = data_scaled_copy[features_with_lables]\n",
    "df_gmm['cluster'] = df_gmm['GmmLabels']\n",
    "df_gmm['cluster_label'] = 'gmm'\n",
    "\n",
    "features_with_lables = [\"Avg_Credit_Limit\",\t\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\",\"Labels\"]\n",
    "df_kmeans = data_scaled_copy[features_with_lables]\n",
    "df_kmeans['cluster'] = df_kmeans['Labels']\n",
    "df_kmeans['cluster_label'] = 'kmeans'\n",
    "\n",
    "df_compare = pd.concat([ df_kmeans, df_gmm], axis=0)\n",
    "\n",
    "value_cols = [\"Avg_Credit_Limit\",\"Total_Credit_Cards\",\"Total_calls_made\", \"Total_visits_bank\",\"Total_visits_online\"]\n",
    "\n",
    "for col in value_cols:\n",
    "    \n",
    "    sns.boxplot(data=df_compare, x='cluster', y=col, hue='cluster_label').set_title(col)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EHA5nl2yvvPU"
   },
   "source": [
    "**Cluster Profiles:____________**\n",
    "\n",
    "> Cluster 2 has 49 observations, the highest median Avg_Credit_Limit (probably wealthier). These higher Avg_Credit_Limit individuals visits online far more frequently (highest Total_visits_online median) than other clusters, and also have higher median Total_Credit_Cards than other clusters.\n",
    "\n",
    "> The other two clusters are a bit more mixed\n",
    "  > - Cluster 0, has the largest count of observations (374), and has the lowest median Avg_Credit_Limit, lowest median Total_Credit_Cards count, and tend to place more Total_Calls_made (higher median calls) than other clusters (perhaps because of more strained financial circumstances)\n",
    "  \n",
    "  > - Lastly, Cluster 1, has a total count of observations of 221 - they have the highest IQR of Total_Visits_bank and lowest Total_visits_online. But otherwise, are middle of the road between cluster 0 and 2 in terms of Avg_Credit_Limit, Total_Credit_Cards, Total_Calls_made\n",
    "\n",
    "**Comparing Clusters:____________**\n",
    "\n",
    "> A side by side comparison between GMM and K-Means boxplots/clusters appears largely identical - therefore conclusions/profiles amongst two methods are the same. Also can be observed by visually comparing the custom boxplots above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ixzPPJG6vvPV"
   },
   "source": [
    "## K-Medoids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "UxuTu1JF48hA"
   },
   "outputs": [],
   "source": [
    "kmedo = KMedoids(n_clusters = 3, random_state=1) #Apply the K-Medoids algorithm\n",
    "kmedo.fit(data_scaled) #Fit the kmedo function on the scaled data\n",
    "\n",
    "data_scaled_copy['kmedoLabels'] = kmedo.predict(data_scaled)\n",
    "data['kmedoLabels'] = kmedo.predict(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "9t6hVMWs2lez",
    "outputId": "bd84b129-be61-45e3-b8e0-8428caa91e78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    289\n",
       "0    222\n",
       "1    133\n",
       "Name: kmedoLabels, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of observations in each cluster\n",
    "data.kmedoLabels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "FvKJ9rrzvvPW",
    "outputId": "c8f9fc4d-86c7-4d33-d2de-aaaba2f84d0f"
   },
   "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>group_0 Mean</th>\n",
       "      <th>group_1 Mean</th>\n",
       "      <th>group_2 Mean</th>\n",
       "      <th>group_0 Median</th>\n",
       "      <th>group_1 Median</th>\n",
       "      <th>group_2 Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg_Credit_Limit</th>\n",
       "      <td>12216.216216</td>\n",
       "      <td>85052.631579</td>\n",
       "      <td>28449.826990</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <td>2.423423</td>\n",
       "      <td>7.030075</td>\n",
       "      <td>5.363322</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <td>0.950450</td>\n",
       "      <td>1.691729</td>\n",
       "      <td>3.830450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_online</th>\n",
       "      <td>3.554054</td>\n",
       "      <td>4.639098</td>\n",
       "      <td>0.982699</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_calls_made</th>\n",
       "      <td>6.878378</td>\n",
       "      <td>1.969925</td>\n",
       "      <td>1.851211</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     group_0 Mean  group_1 Mean  group_2 Mean  group_0 Median  \\\n",
       "Avg_Credit_Limit     12216.216216  85052.631579  28449.826990         12000.0   \n",
       "Total_Credit_Cards       2.423423      7.030075      5.363322             2.0   \n",
       "Total_visits_bank        0.950450      1.691729      3.830450             1.0   \n",
       "Total_visits_online      3.554054      4.639098      0.982699             4.0   \n",
       "Total_calls_made         6.878378      1.969925      1.851211             7.0   \n",
       "\n",
       "                     group_1 Median  group_2 Median  \n",
       "Avg_Credit_Limit            68000.0         20000.0  \n",
       "Total_Credit_Cards              7.0             5.0  \n",
       "Total_visits_bank               2.0             4.0  \n",
       "Total_visits_online             2.0             1.0  \n",
       "Total_calls_made                2.0             2.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating summary statistics of the original data for each label\n",
    "mean = data.groupby('kmedoLabels').mean()\n",
    "median = data.groupby('kmedoLabels').median()\n",
    "df_kmedoids = pd.concat([mean, median], axis=0)\n",
    "df_kmedoids.index = ['group_0 Mean', 'group_1 Mean', 'group_2 Mean', 'group_0 Median', 'group_1 Median', 'group_2 Median']\n",
    "df_kmedoids[original_features].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "eWdxV2rLvvPX",
    "outputId": "c93561c9-22a3-4181-a2c3-b2b473099d25"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting boxplots with the new K-Medoids based labels\n",
    "\n",
    "features_with_lables = [\"Avg_Credit_Limit\",\t\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\",\"kmedoLabels\"]\n",
    "\n",
    "data_scaled_copy[features_with_lables].boxplot(by = 'kmedoLabels', layout = (1,5),figsize=(20,7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B_MtSZkr2le0"
   },
   "source": [
    "Let's compare the clusters from K-Means and K-Medoids "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "l1gebsJ_2le0",
    "outputId": "288db421-b425-4f7f-ea68-7ab9d067250c"
   },
   "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>Avg_Credit_Limit</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_calls_made</th>\n",
       "      <th>Total_calls_made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_0 Mean</th>\n",
       "      <td>12216.216216</td>\n",
       "      <td>12239.819005</td>\n",
       "      <td>2.423423</td>\n",
       "      <td>2.411765</td>\n",
       "      <td>0.950450</td>\n",
       "      <td>0.945701</td>\n",
       "      <td>3.554054</td>\n",
       "      <td>3.561086</td>\n",
       "      <td>6.878378</td>\n",
       "      <td>6.891403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_1 Mean</th>\n",
       "      <td>85052.631579</td>\n",
       "      <td>33893.048128</td>\n",
       "      <td>7.030075</td>\n",
       "      <td>5.508021</td>\n",
       "      <td>1.691729</td>\n",
       "      <td>3.489305</td>\n",
       "      <td>4.639098</td>\n",
       "      <td>0.975936</td>\n",
       "      <td>1.969925</td>\n",
       "      <td>1.997326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_2 Mean</th>\n",
       "      <td>28449.826990</td>\n",
       "      <td>140102.040816</td>\n",
       "      <td>5.363322</td>\n",
       "      <td>8.775510</td>\n",
       "      <td>3.830450</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>0.982699</td>\n",
       "      <td>10.979592</td>\n",
       "      <td>1.851211</td>\n",
       "      <td>1.102041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_0 Median</th>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_1 Median</th>\n",
       "      <td>68000.000000</td>\n",
       "      <td>31500.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_2 Median</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>145000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Avg_Credit_Limit  Avg_Credit_Limit  Total_Credit_Cards  \\\n",
       "group_0 Mean        12216.216216      12239.819005            2.423423   \n",
       "group_1 Mean        85052.631579      33893.048128            7.030075   \n",
       "group_2 Mean        28449.826990     140102.040816            5.363322   \n",
       "group_0 Median      12000.000000      12000.000000            2.000000   \n",
       "group_1 Median      68000.000000      31500.000000            7.000000   \n",
       "group_2 Median      20000.000000     145000.000000            5.000000   \n",
       "\n",
       "                Total_Credit_Cards  Total_visits_bank  Total_visits_bank  \\\n",
       "group_0 Mean              2.411765           0.950450           0.945701   \n",
       "group_1 Mean              5.508021           1.691729           3.489305   \n",
       "group_2 Mean              8.775510           3.830450           0.591837   \n",
       "group_0 Median            2.000000           1.000000           1.000000   \n",
       "group_1 Median            6.000000           2.000000           3.000000   \n",
       "group_2 Median            9.000000           4.000000           1.000000   \n",
       "\n",
       "                Total_visits_online  Total_visits_online  Total_calls_made  \\\n",
       "group_0 Mean               3.554054             3.561086          6.878378   \n",
       "group_1 Mean               4.639098             0.975936          1.969925   \n",
       "group_2 Mean               0.982699            10.979592          1.851211   \n",
       "group_0 Median             4.000000             4.000000          7.000000   \n",
       "group_1 Median             2.000000             1.000000          2.000000   \n",
       "group_2 Median             1.000000            11.000000          2.000000   \n",
       "\n",
       "                Total_calls_made  \n",
       "group_0 Mean            6.891403  \n",
       "group_1 Mean            1.997326  \n",
       "group_2 Mean            1.102041  \n",
       "group_0 Median          7.000000  \n",
       "group_1 Median          2.000000  \n",
       "group_2 Median          1.000000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pd.concat([df_kmedoids, df_kmeans], axis=1)[original_features]\n",
    "comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l2268li0bkjecTuv2nbj3l4/UWqY+higvKCigsrKS1atXM2HCBF566aWUtZUIJQsRyUpbNqzl5iOWJq2+XyxPWlVJ0blz54xJFKBuKBGRg7JixQoGDRrElClTOPfcczn77LPp2bMnjzzyCA888ACDBg1i2LBhbNoUm6Bp+fLljBo1iiFDhnDSSSexdGks0X355ZcMHz6c4447jjvuuGNf/WVlZRxzzDH7lk866SQGDx7M4MGD+dvf/gbA+++/z4gRI7jgggs48sgjueyyy6h+0PqWW27h6KOPpn///kyaNKnO71dnFiIiCUrmEOUTJ07kmmuu4corr+TXv/51je117NiRt99+m/z8fJYtW8Yll1xC9SgV8+bNY/HixXTu3JkTTjiBDz/8kKOPPppXX32VpUuXYmZs3ry5zu9ZZxYiIgmoHqL86aefZuDAgQD7hijv0KHD94YoLysr+84Q5QMHDuTqq69mzZrY7NAffvghl1xyCQBXXHFFjW3u3r2bH//4xxx77LGMHTuWzz//fN++oUOH0qVLF3Jychg4cCBlZWW0atWK/Px8fvSjH/HKK6/QvHnzOr9vnVmIiCQg2UOUA5jVOmEoDz74IJ06dWLBggVUVVWRn5+/b19Nw5g3adKETz75hHfffZfnnnuORx55hNmzZx/sW469lzodLSLSyFQPUT5jxgyeffbZSMfED1EO4O4sWLAAgBNOOIHnnnsOiI1MW5MtW7Zw+OGHk5OTw8yZM9m7t/aJzyorK9myZQujR4/mV7/61QGTVCLSfmZhZk8AZwHr3P2YGvaPAP4bqB639xV3/3m9BSgiGal1+05JvYOpdftOkcsmc4jyqVOncumllzJ16lTOP//8Go8dP348559/Pi+++CKnnHJK6LTKFRUVjBkzhp07d+LuPPjgg5Hf24GkfYhyM/sHoBKYUUuymOTuZ0WtU0OUZw4NUS7JoiHKkyvrhih39w+ATemOQ0REDiztySKi4Wa2wMz+bGb9aipgZuPMrNjMitevX1/f8YmINGjZkCzmAt3dfQDwMPCHmgq5+3R3L3L3og4dOtRnfCJST9Ldbd5QHMznmPHJwt23untlsDwLyDOz9mkOS0TqWX5+Phs3blTCqCN3Z+PGjd+5/TaKtN8NFcbMDgPWurub2VBiCW5jmsMSkXrWpUsXysvLUTdz3eXn59OlS5eEjkl7sjCz3wMjgPZmVg7cBeQBuPs04ALgGjPbA+wALnb9aSHS6OTl5dGzZ890h9FopT1ZuPslIfsfAWofN1hERFIq469ZiIhI+ilZiIhIKCULEREJpWQhIiKhlCxERCSUkoWIiIRSshARkVBKFiIiEkrJQkREQilZiIhIKCULEREJpWQhIiKhIg8kaGYnAn3c/Ukz6wAUuPuXqQutfqxatYrc7VtotnRWukOJJHf7RrZX7eGrnFzundsq3eGE+qoilxarVqU7DBGpo0hnFmZ2F3AzcGuwKQ94OlVBiYhIZol6ZvFPwCBiU5zi7qvNrGXKoqpHhYWFfPNtE3YcOTrdoUTSbOksCqoq6Jq3mdsGb013OKHunduKpoWF6Q5DROoo6jWLXcGEQw5gZi1SF5KIiGSaqMniBTP7DdDGzH4MvAP8NnVhiYhIJonUDeXu95vZ6cBWoC9wp7u/ndLIREQkY0S+GypIDkoQIiKNUK3JwswqCK5T1MTdM//eTRERqbNak4W7twQws58D3wAzAQMuA5JyN5SZPQGcBaxz92Nq2G/AVGA0sB34F3efm4y2RUQkmqgXuEe6+6PuXuHuW939MeD8JMXwO2BULfvPBPoEr3HAY0lqV0REIoqaLPaa2WVmlmtmOWZ2GbA3GQG4+wfAplqKjAFmeMwcYndkHZ6MtkVEJJqoyeJS4EJgbfAaG2yrD4XA13Hr5cG27zCzcWZWbGbF69evr6fQREQah6i3zpYR+ws/HayGbd+76O7u04HpAEVFRQe8KC8iIomLlCzMLB/4N6AfkF+93d3/NUVxxSsHusatdwFW10O7IiISiNoNNRM4DBgJ/IXYL+yKVAW1n9eAKy1mGLDF3dfUU9siIkL0h/J6u/tYMxvj7k+Z2bPAm8kIwMx+D4wA2ptZOXAXsVFtcfdpwCxit82WErt19qpktCsiItFFTRa7g5+bzewYYs9c9EhGAO5+Sch+B36SjLZEROTgRE0W083sUOAOYt1CBcCdKYtKREQyStS7oR4PFv8C9EpdOCIikomi3g3VBriSWNfTvmPcfUJKohIRkYwStRtqFjAH+AyoSl04IiKSiaImi3x3vyGlkYiISMaK/JyFmf3YzA43s7bVr5RGJiIiGSPqmcUuYApwO38fasPRxW4RkUYharK4gdiDeRtSGYyIiGSmqN1Qi4k9PS0iIo1Q1DOLvcB8M3sP+LZ6o26dFRFpHKImiz8ELxERaYSiPsH9VG37zexld0/WNKsiIpJhol6zCKO7okREGrBkJQvNTCci0oAlK1mIiEgDlqxkUdM82SIi0kAknCzM7FAz67/f5puTFI+IiGSgSMnCzN43s1bBeFALgCfN7IHq/e7+VqoCFBGR9It6ZtHa3bcC5wFPuvsQ4IepC0tERDJJ1GTRxMwOBy4E/pTMAMxslJl9YWalZnZLDftHmNkWM5sfvDSdq4hIPYv6BPfPgTeBv7r7p2bWC1hW18bNLBf4NXA6UA58amavufvn+xX9v+5+Vl3bExGRgxP1Ce4XgRfj1lcAyXhieyhQGtSHmT0HjAH2TxYiIpJGtSYLM3uYWh64S8JAgoXA13Hr5cDxNZQbbmYLgNXAJHdfXEOs44BxAN26dUsoiNztm2i2dFZCx4TJ2bkVgKr8VkmtN3f7JsjPY2VlLvfOTW7da7fHeiU7NU/ezLkrK3Ppk7TaRCRdws4silPcfk3PZ+yfnOYC3d290sxGExvQ8Hu/f9x9OjAdoKioKPIT5b17944cbCJKSyti9ffqlOSaO7Ft2zZatEh+3LtKSwFo2j15dfchdZ+xiNSfWpNF2ACCSVAOdI1b70Ls7CE+hq1xy7PM7FEza5+siZiuu+66ZFTzPRMnTgRg6tSpKak/FbIxZhGpH2HdUH+k9m6oc+rY/qdAHzPrCawCLgYu3S+Gw4C17u5mNpTYHVwb69iuiIgkIKwb6v5UNu7ue8zsWmJ3WuUCT7j7YjP792D/NOAC4Boz2wPsAC52dw1cKCJSj8K6of6S6gDcfRYwa79t0+KWHwEeSXUcIiJyYJFunTWzPsB/AEcD+dXb3V3zWIiINAJRn+B+EngM2AOcAswAZqYqKBERySxRk0Uzd38XMHf/yt0nA6emLiwREckkUYf72GlmOcCy4IL0KqBj6sISEZFMEvXM4nqgOTABGAJcDvxzimISEZEME3VsqE+DxUrgqtSFIyIimSjq5Edvm1mbuPVDzezNlEUlIiIZJWo3VHt331y94u7/g65ZiIg0GlGTRZWZ7RvK1cy6U8swICIi0rBEvRvqduCvZlb9RPc/EAwHLiIiDV/UC9xvmNlgYBixYcV/Gj/qq5n1q2mOCRERaRiinlkQJIcDzb89ExiclIhERCTjRL1mEaamSYxERKSBSFay0MVuEZEGLHI3lEi2e/jhhykNpo4Ns2rVKgAKCwtDy/bu3TslMy6mKl5IXczydw3t+0tWstiVpHpEMsKOHTvSHUJCsi1e+a5s+P7CplWt9aK1u88Nfg5LZlAiqZDIX2KZMB95tsUr39XQvr+wM4tf1rLP0TDlIiKNQti0qqfUVyAiIpK5Il+zMLNj+P60qjPqGoCZjQKmArnA4+5+3377Ldg/GtgO/Et195eIiNSPqHNw3wWMIJYsZgFnAn8lNr3qQTOzXODXwOlAOfCpmb3m7p/HFTsT6BO8jic2vevxdWlXREQSE/U5iwuA04Bv3P0qYADQNAntDwVK3X2Fu+8CngPG7FdmDDDDY+YAbczs8CS0LSIiEUVNFjvcvQrYY2atgHVAryS0Xwh8HbdeHmxLtIyIiKRQ1GsWxcHkR78FSojNmPdJEtqvaZiQ/Z8Gj1IGMxtHMBJut27dvneAiIgcvKijzo4PFqeZ2RtAK3dfmIT2y4GucetdgNUHUQZ3nw5MBygqKtLwIyIiSRR1WtV3q5fdvczdF8Zvq4NPgT5m1tPMDgEuBl7br8xrwJUWMwzY4u5rktC2iIhEFPYEdz7QHGhvZofy9y6hVkDnujbu7nvM7FrgTWK3zj7h7ovN7N+D/dOI3X01GiglduvsVXVtV0REEhPWDXU1cD2xxBD/bMNWYre81pm7zyKWEOK3TYtbduAnyWhLREQOTtgT3FOBqWZ2nbs/XE8xiYhIhol6N9RvzGwCsbm3Ad4HfuPuu1MSlYiIZJSoyeJRIC/4CXAFsSepf5SKoEREJLOEXeBu4u57gOPcfUDcrtlmtiC1oYmISKYIu3W2+sG7vWZ2RPVGM+sF7E1ZVCIiklHCuqGqb5WdBLxnZiuC9R7oFlYRkUYjLFl0MLMbguXfEHsWYhuxYcoHAe+lMDaRUInMc5yI6jqrZzBLlm3bttGiRYuk1gmpixcyY77uTJnPOtv+vUHyvr+wZJELFPDd8ZkKgp8t69y6SB2VlpYyf9ES9jZvm9R6c3bFRowpWbE2aXXmbt9EQX4e/m0F3QqS24t7yO5Yj/K3XxUntd6VlblJra8+pHI+69LSUpYtntcov7+wZLHG3X+etNZEUmBv87bsOHJ0usMI1WzpLKiKJYrbBm9NdziR3Du3VbpDADJrPuvG+v2FXeCuacRXERFpZMKSxWn1EoWIiGS0WpOFu2+qr0BERCRzRZ0pT0REGjElCxERCaVkISIioZQsREQklJKFiIiEUrIQEZFQShYiIhJKyUJEREJFnSkv6cysLfA8seHOy4AL3f1/aihXBlQQmz9jj7sX1V+UIiIC6T2zuAV41937AO8G6wdyirsPVKIQEUmPdCaLMcBTwfJTwLnpC0VERGqTzmTRyd3XAAQ/Ox6gnANvmVmJmY07UGVmNs7Mis2seP369SkIV0Sk8UrpNQszewc4rIZdtydQzQnuvtrMOgJvm9lSd/9g/0LuPh2YDlBUVOQHFbCIiNQopcnC3X94oH1mttbMDnf3NWZ2OLDuAHWsDn6uM7NXgaHA95KFNE6rVq0id/uW2MRCGS53+0a2V+3hq5zcjJlUKMxXFbm0CKYpTbZsnKL0iy++wHY3zu8vbXdDAa8B/wzcF/z87/0LmFkLIMfdK4LlMwDN3CfSAGTTlLgQmxY3z6rIS2qt2SOdyeI+4AUz+zdgJTAWwMw6A4+7+2igE/CqmUEs1mfd/Y00xSsZqLCwkG++bZI106oWVFXQNW9zVk3L2bSwMGX1Z8uUuBD//e1olN9f2pKFu2+khpn4gm6n0cHyCmBAPYcmIiL70RPcIiISSslCRERCKVmIiEgoJQsREQmlZCEiIqGULEREJJSShYiIhFKyEBGRUEoWIiISSslCRERCKVmIiEgoJQsREQmlZCEiIqGULEREJJSShYiIhFKyEBGRUOmcKS/rJDJncKLzAPfu3ZvrrrvuoGM7kGyMOVG52zclfQ7unJ2xmdCq8pM313Lu9k2Qn8fKyuTP4bx2e+zvvk7Nq5Ja78rKXPoktca/y6b50yE2h/q35qz8tnF+f0oWKdKsWbN0h5CwbIy5d+/eKam3tLQiVn+vTkmstRPbtm2jRYvkx7wrSPRNuye37j6k7jPORk2aNKFP335Jrzcbvj9z96RUlEmKioq8uLg43WFIFqs+u5o6dWqaI4km2+KFWMwlK9Zm1RzcQ3p1SslnnCnfn5mVuHtRTfvSds3CzMaa2WIzqzKzGoMLyo0ysy/MrNTMbqnPGEVEJCadF7gXAecBHxyogJnlAr8GzgSOBi4xs6PrJzwREamWtmsW7r4EwMxqKzYUKHX3FUHZ54AxwOcpD1BERPbJ9FtnC4Gv49bLg23fY2bjzKzYzIrXr19fL8GJiDQWKT2zMLN3gMNq2HW7u/93lCpq2FbjFXl3nw5Mh9gF7shBiohIqJQmC3f/YR2rKAe6xq13AVbXsU4REUlQpndDfQr0MbOeZnYIcDHwWppjEhFpdNJ56+w/mVk5MBx43czeDLZ3NrNZAO6+B7gWeBNYArzg7ovTFbOISGOVzruhXgVerWH7amB03PosIDvGAxARaaAyvRtKREQygJKFiIiEUrIQEZFQShYiIhJKyUJEREIpWYiISCglCxERCaVkISIioTStqjQaqZqPXPOnH7yo86fn7NyKVe1OSQyekxdprvXc7ZuA6NPsNrTvT8lCpAbZNh95tsULic0NvWrVHnbs2JGSOJo1a0ZhYZQk0Cll85Fnw/enObhFRATI0Dm4RUQkeyhZiIhIKCULEREJpWQhIiKhlCxERCSUkoWIiIRSshARkVBKFiIiEqpBPpRnZuuBr9IdRwq1BzakOwg5aPr+sldD/+66u3uHmnY0yGTR0JlZ8YGespTMp+8vezXm707dUCIiEkrJQkREQilZZKfp6Q5A6kTfX/ZqtN+drlmIiEgonVmIiEgoJQsREQmlZJFlzGyUmX1hZqVmdku645HozOwJM1tnZovSHYskxsy6mtl7ZrbEzBabWbT5TxsQXbPIImaWC/w/4HSgHPgUuMTdP09rYBKJmf0DUAnMcPdj0h2PRGdmhwOHu/tcM2sJlADnNqb/ezqzyC5DgVJ3X+Huu4DngDFpjkkicvcPgE3pjkMS5+5r3H1usFwBLAEK0xtV/VKyyC6FwNdx6+U0sn+wIulmZj2AQcDHaQ6lXilZZBerYZv6EUXqiZkVAC8D17v71nTHU5+ULLJLOdA1br0LsDpNsYg0KmaWRyxRPOPur6Q7nvqmZJFdPgX6mFlPMzsEuBh4Lc0xiTR4ZmbAfwFL3P2BdMeTDkoWWcTd9wDXAm8Su8D2grsvTm9UEpWZ/R74COhrZuVm9m/pjkkiOwG4AjjVzOYHr9HpDqo+6dZZEREJpTMLEREJpWQhIiKhlCxERCSUkoWIiIRSshARkVBKFiJ1ZGaTzWzSQRzXxszGpyImkWRTshBJnzZAQsnCYvT/Vuqd/tGJJMjMrjSzhWa2wMxm7rfvfTMrCpbbm1lZsNzPzD4JHuZaaGZ9gPuAI4JtU4JyN5nZp0GZnwXbegTzKDwKzOW7Q76I1Ism6Q5AJJuYWT/gduAEd99gZm2BCREO/Xdgqrs/EwzVkgvcAhzj7gODus8A+hAbit6A14I5MFYCfYGr3F3dVpIWShYiiTkVeMndNwC4+6bYsEGhPgJuN7MuwCvuvqyG484IXvOC9QJiyWMl8JW7z0lC/CIHRd1QIokxah8Wfg9//3+VX73R3Z8FzgF2AG+a2akHqPs/3H1g8Ort7v8V7NtW99BFDp6ShUhi3gUuNLN2AEE3VLwyYEiwfEH1RjPrBaxw94eIjRTcH6gAWsYd+ybwr8GcCZhZoZl1TMWbEEmUuqFEEuDui83sHuAvZraXWJdRWVyR+4EXzOwKYHbc9ouAy81sN/AN8POgC+tDM1sE/NndbzKzo4CPgi6qSuByYG/K35hICI06KyIiodQNJSIioZQsREQklJKFiIiEUrIQEZFQShYiIhJKyUJEREIpWYiISKj/D2WoG46alN+LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Jose Medina - custom analysis shows both plot side by side\n",
    "\n",
    "features_with_lables = [\"Avg_Credit_Limit\",\t\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\",\"kmedoLabels\"]\n",
    "df_kmedo = data_scaled_copy[features_with_lables]\n",
    "df_kmedo['cluster'] = df_kmedo['kmedoLabels']\n",
    "df_kmedo['cluster_label'] = 'kmedians'\n",
    "\n",
    "features_with_lables = [\"Avg_Credit_Limit\",\t\"Total_Credit_Cards\",\"Total_visits_bank\",\"Total_visits_online\",\"Total_calls_made\",\"Labels\"]\n",
    "df_kmeans = data_scaled_copy[features_with_lables]\n",
    "df_kmeans['cluster'] = df_kmeans['Labels']\n",
    "df_kmeans['cluster_label'] = 'kmeans'\n",
    "\n",
    "df_compare = pd.concat([ df_kmeans, df_kmedo], axis=0)\n",
    "\n",
    "value_cols = [\"Avg_Credit_Limit\",\"Total_Credit_Cards\",\"Total_calls_made\", \"Total_visits_bank\",\"Total_visits_online\"]\n",
    "\n",
    "for col in value_cols:\n",
    "    \n",
    "    sns.boxplot(data=df_compare, x='cluster', y=col, hue='cluster_label').set_title(col)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iuNbMOGKvvPY"
   },
   "source": [
    "**Cluster Profiles:____________**\n",
    "\n",
    "> Clusters 0, 1, and 2 are much more evenly distributed with K-Medoids \n",
    "> - Cluster 2:    289,\n",
    "> - Cluster 0:    222 \n",
    "> - Cluster 1:    133\n",
    "\n",
    "> Cluster 2 has 289 observations, the highest Total_visits_bank, and lowest Total_visits_online (the opposite conclusion from K-means!). Their Avg_Credit_Limit and Total_Credit_Cards sits between Cluster 0 and 1 (middle of the road) and they place relatively few calls (as low as cluster 1). With K-Means, Cluster 2 had the highest Avg_Credit_Limit, but with K-Medoids, it is now the second highest.\n",
    "\n",
    "> Cluster 0, still has the lowest Avg_Credit_Limit (consistent with K-Means conclusion), and Lowest_Total_Credit_Cards, and amongst the lowest Total_Visits_bank. They have the highest Total_Calls_Made (consistent between K-means and K-medoids). In general - Cluster 0 positions on Boxplot did NOT change materially with respect to itself, but other clusters did change.\n",
    "\n",
    "> Cluster 1 experienced a big change in terms of Avg_Credit_Limit - with K-Medoids, Cluster 1 now has the highest Avg_Credit_Limit of all clusters. Cluster 1 now also has the highest Total_Credit_Cards, and biggest IQR for Total_visits_online\n",
    "\n",
    "**Comparing Clusters:____________**\n",
    "\n",
    "> A side by side comparison reveals the following changes (consistent with my prior observation that non-normal distributions would largely skew the results of K-Means)\n",
    "> - Cluster 2 - Total_visits_bank, and lowest Total_visits_online (the opposite conclusion from K-means!), With K-Means, Cluster 2 had the highest Avg_Credit_Limit, but with K-Medoids, it is now only the second highest.\n",
    "> - Cluster 0 positions on Boxplot did NOT change materially with respect to itself between K-Means and K-Medoids. This suggests that cluster 0, as a subset, may have a distribution that approximates gaussian or at least has proximate distributions, median, and means describing their distribution.\n",
    "> - Cluster 1 experiences large changes between methodologies - with K-Medoids, Cluster 1 now has the highest Avg_Credit_Limit of all clusters. Cluster 1 now also has the highest Total_Credit_Cards, and biggest IQR for Total_visits_onlin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Learner_Notebook_Unsupervised_Learning_Project.ipynb",
   "provenance": []
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
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}