Unsupervised Project 6.jstatter.ipynb

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    "Jennifer Statter\n",
    "\n",
    "#### Background: \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 customer 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",
    "To identify different segments in the existing customer, based on their spending patterns as well as past interaction with the bank, using clustering algorithms, and provide recommendations to the bank on how to better market to and service these customers.\n",
    "\n",
    "#### Feature Dictionary:\n",
    "* Sl_No: Primary key of the records\n",
    "* Customer Key: Customer identification number\n",
    "* Average Credit Limit: Average credit limit of each customer for all credit cards\n",
    "* Total credit cards: Total number of credit cards possessed by the customer\n",
    "* Total visits bank: Total number of visits that customer made (yearly) personally to the bank\n",
    "* Total visits online: Total number of visits or online logins made by the customer (yearly)\n",
    "* Total calls made: Total number of calls made by the customer to the bank or its customer service department (yearly)"
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    "### Interpretation:\n",
    "\n",
    "#### Marketing Statement: \n",
    "The Bank wants to upsell credit cards with the goal to increase the amount of responsible customers that earned a higher credit card limit. Therefore, our target group is Total credit cards. \n",
    "\n",
    "#### Delivery Statement:\n",
    "Use Total credit cards in relation to bank visits, visits online, and calls to make a decision on what service to focus on.   "
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    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# to compute distances\n",
    "from scipy.spatial.distance import pdist\n",
    "from scipy.spatial.distance import cdist\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, cophenet\n",
    "\n",
    "from scipy.stats import zscore\n",
    "\n",
    "data = pd.read_csv(\"Credit+Card+Customer+Data.csv\")\n",
    "data.dtypes\n",
    "\n",
    "# To supress warnings\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# This will help in making the Python code more structured automatically (good coding practice)\n",
    "%load_ext nb_black\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
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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",
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       "      <th>0</th>\n",
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       "      <td>87073</td>\n",
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       "      <th>1</th>\n",
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       "      <td>38414</td>\n",
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       "    <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",
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       "      <th>4</th>\n",
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       "      <td>47437</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
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      "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  "
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       "Sl_No                  0\n",
       "Customer Key           0\n",
       "Avg_Credit_Limit       0\n",
       "Total_Credit_Cards     0\n",
       "Total_visits_bank      0\n",
       "Total_visits_online    0\n",
       "Total_calls_made       0\n",
       "dtype: int64"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 660 entries, 0 to 659\n",
      "Data columns (total 5 columns):\n",
      " #   Column               Non-Null Count  Dtype\n",
      "---  ------               --------------  -----\n",
      " 0   Avg_Credit_Limit     660 non-null    int64\n",
      " 1   Total_Credit_Cards   660 non-null    int64\n",
      " 2   Total_visits_bank    660 non-null    int64\n",
      " 3   Total_visits_online  660 non-null    int64\n",
      " 4   Total_calls_made     660 non-null    int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 25.9 KB\n"
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    "* I have no dupicates. I dropped all useless columns. My memory usage is small and manageable. I have no nulls. All my data types are int64. I am now ready to do outlier treatment.  "
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   "source": [
    "#### Outlier Identification Using Univariate Analysis "
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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": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"plt.hist(data[\\\"Avg_Credit_Limit\\\"], 20)\\nplt.title(\\\"Avg_Credit_Limit Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Avg_Credit_Limit\\\"])\\nplt.title(\\\"Avg_Credit_Limit\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.hist(data[\\\"Avg_Credit_Limit\\\"], 20)\\nplt.title(\\\"Avg_Credit_Limit Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Avg_Credit_Limit\\\"])\\nplt.title(\\\"Avg_Credit_Limit\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data[\"Avg_Credit_Limit\"], 20)\n",
    "plt.title(\"Avg_Credit_Limit Histogram\")\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(data[\"Avg_Credit_Limit\"])\n",
    "plt.title(\"Avg_Credit_Limit\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5714a225",
   "metadata": {},
   "source": [
    "* Avg_Credit_Limit is right-scewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4dab6f8",
   "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": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"plt.hist(data[\\\"Total_visits_bank\\\"], 20)\\nplt.title(\\\"Total_visits_bank Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Total_visits_bank\\\"])\\nplt.title(\\\"Total_visits_bank\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.hist(data[\\\"Total_visits_bank\\\"], 20)\\nplt.title(\\\"Total_visits_bank Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Total_visits_bank\\\"])\\nplt.title(\\\"Total_visits_bank\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data[\"Total_visits_bank\"], 20)\n",
    "plt.title(\"Total_visits_bank Histogram\")\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(data[\"Total_visits_bank\"])\n",
    "plt.title(\"Total_visits_bank\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad21dbf",
   "metadata": {},
   "source": [
    "* Vists to the bank have a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c68a2da",
   "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": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"plt.hist(data[\\\"Total_visits_online\\\"], 20)\\nplt.title(\\\"Total_visits_online_bank Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Total_visits_online\\\"])\\nplt.title(\\\"Total_visits_online\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.hist(data[\\\"Total_visits_online\\\"], 20)\\nplt.title(\\\"Total_visits_online_bank Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Total_visits_online\\\"])\\nplt.title(\\\"Total_visits_online\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data[\"Total_visits_online\"], 20)\n",
    "plt.title(\"Total_visits_online_bank Histogram\")\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(data[\"Total_visits_online\"])\n",
    "plt.title(\"Total_visits_online\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38db0cb6",
   "metadata": {},
   "source": [
    "* Visits Online are right-scewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47b3b524",
   "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": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"plt.hist(data[\\\"Total_calls_made\\\"], 20)\\nplt.title(\\\"Total_calls_made_bank Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Total_calls_made\\\"])\\nplt.title(\\\"Total_calls_made\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.hist(data[\\\"Total_calls_made\\\"], 20)\\nplt.title(\\\"Total_calls_made_bank Histogram\\\")\\nplt.show()\\n\\nsns.boxplot(data[\\\"Total_calls_made\\\"])\\nplt.title(\\\"Total_calls_made\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data[\"Total_calls_made\"], 20)\n",
    "plt.title(\"Total_calls_made_bank Histogram\")\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(data[\"Total_calls_made\"])\n",
    "plt.title(\"Total_calls_made\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc813fd",
   "metadata": {},
   "source": [
    "* Calls made are right-scewed slightly "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f9a341",
   "metadata": {},
   "source": [
    "There will be no outlier treatment because we are dealing with moneny for credit card limit. However, there is a need for down scaling the credit card limit column because the rest of the columns are less then 15 units. Since I will be down scaling it, I will need it to be repesented by a float to account for the hundreds and thousands of dollers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1572dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# function to plot a boxplot and a histogram along the same scale.\\n\\n\\ndef histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\\n    \\\"\\\"\\\"\\n    Boxplot and histogram combined\\n\\n    data: dataframe\\n    feature: dataframe column\\n    figsize: size of figure (default (12,7))\\n    kde: whether to the show density curve (default False)\\n    bins: number of bins for histogram (default None)\\n    \\\"\\\"\\\"\\n    f2, (ax_box2, ax_hist2) = plt.subplots(\\n        nrows=2,  # Number of rows of the subplot grid= 2\\n        sharex=True,  # x-axis will be shared among all subplots\\n        gridspec_kw={\\\"height_ratios\\\": (0.25, 0.75)},\\n        figsize=figsize,\\n    )  # creating the 2 subplots\\n    sns.boxplot(\\n        data=data, x=feature, ax=ax_box2, showmeans=True, color=\\\"violet\\\"\\n    )  # boxplot will be created and a star will indicate the mean value of the column\\n    sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\\\"winter\\\"\\n    ) if bins else sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2\\n    )  # For histogram\\n    ax_hist2.axvline(\\n        data[feature].mean(), color=\\\"green\\\", linestyle=\\\"--\\\"\\n    )  # Add mean to the histogram\\n    ax_hist2.axvline(\\n        data[feature].median(), color=\\\"black\\\", linestyle=\\\"-\\\"\\n    )  # Add median to the histogram\";\n",
       "                var nbb_formatted_code = \"# function to plot a boxplot and a histogram along the same scale.\\n\\n\\ndef histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\\n    \\\"\\\"\\\"\\n    Boxplot and histogram combined\\n\\n    data: dataframe\\n    feature: dataframe column\\n    figsize: size of figure (default (12,7))\\n    kde: whether to the show density curve (default False)\\n    bins: number of bins for histogram (default None)\\n    \\\"\\\"\\\"\\n    f2, (ax_box2, ax_hist2) = plt.subplots(\\n        nrows=2,  # Number of rows of the subplot grid= 2\\n        sharex=True,  # x-axis will be shared among all subplots\\n        gridspec_kw={\\\"height_ratios\\\": (0.25, 0.75)},\\n        figsize=figsize,\\n    )  # creating the 2 subplots\\n    sns.boxplot(\\n        data=data, x=feature, ax=ax_box2, showmeans=True, color=\\\"violet\\\"\\n    )  # boxplot will be created and a star will indicate the mean value of the column\\n    sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\\\"winter\\\"\\n    ) if bins else sns.histplot(\\n        data=data, x=feature, kde=kde, ax=ax_hist2\\n    )  # For histogram\\n    ax_hist2.axvline(\\n        data[feature].mean(), color=\\\"green\\\", linestyle=\\\"--\\\"\\n    )  # Add mean to the histogram\\n    ax_hist2.axvline(\\n        data[feature].median(), color=\\\"black\\\", linestyle=\\\"-\\\"\\n    )  # Add median to the histogram\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to plot a boxplot and a histogram along the same scale.\n",
    "\n",
    "\n",
    "def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\n",
    "    \"\"\"\n",
    "    Boxplot and histogram combined\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    figsize: size of figure (default (12,7))\n",
    "    kde: whether to the show density curve (default False)\n",
    "    bins: number of bins for histogram (default None)\n",
    "    \"\"\"\n",
    "    f2, (ax_box2, ax_hist2) = plt.subplots(\n",
    "        nrows=2,  # Number of rows of the subplot grid= 2\n",
    "        sharex=True,  # x-axis will be shared among all subplots\n",
    "        gridspec_kw={\"height_ratios\": (0.25, 0.75)},\n",
    "        figsize=figsize,\n",
    "    )  # creating the 2 subplots\n",
    "    sns.boxplot(\n",
    "        data=data, x=feature, ax=ax_box2, showmeans=True, color=\"violet\"\n",
    "    )  # boxplot will be created and a star will indicate the mean value of the column\n",
    "    sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\"winter\"\n",
    "    ) if bins else sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2\n",
    "    )  # For histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].mean(), color=\"green\", linestyle=\"--\"\n",
    "    )  # Add mean to the histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].median(), color=\"black\", linestyle=\"-\"\n",
    "    )  # Add median to the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f9859f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QpYVJQycVOgQREcmAErq0oLusiYiUJu1yFxERCQEldGnhn+//Z/75/n8udBgiItJF2uUuLdTtrCt0CCIikgFtoYuIiISAErqIiEgIKKGLiIiEgI6hSwsnjDyh0CGIiEgGlNClhR+c+oNChyAiIhnQLncREZEQyFlCN7PbzGyzmS1LKbvOzNaZ2ZLgcVbKa9eY2SozW2Fmp+cqLunY+fedz/n3nV/oMEREpItyucv9DuCnwNxW5f/l7j9JLTCzo4AZwNHAcOBJMzvC3eM5jE/asPW9rYUOQUREMpCzLXR3fwbYlmb1c4F73L3R3d8GVgFTcxWbiIhI2BTiGPpXzezlYJd8/6BsBLA2pU5dUHYQM7vUzBaZ2aL6+vpcxyoiIlIS8p3QfwGMByYBG4D/DMqtjbre1gzcfY67T3H3KTU1NTkJUkREpNTk9W9r7r6pedjMfgk8EozWAaNSqo4E1ucxNAmcMvaUQocgIiIZyGtCN7Nh7r4hGP000HwG/EPAb83sRpInxR0OLMxnbJJ07UevLXQIIiKSgZwldDO7G5gGDDKzOuD/AdPMbBLJ3emrgS8CuPtyM7sPeBWIAZfpDHcREZH05Syhu/tFbRT/bwf1rweuz1U8kp4z7zoTgD9/9s8FjkRERLpCl36VFvY27S10CCIikgFd+lVERCQElNBFRERCQAldREQkBHQMXVo4+4izCx2CiIhkQAldWrjqxKsKHYKIiGRAu9xFRERCQAldWph2xzSm3TGt0GGIiEgXKaGLiIiEgBK6iIhICCihi4iIhIASuoiISAjob2vSwvSjpxc6BBERyUBaCd3MTnL3ZzsrE4jFE/R438n8dWU9OxtiRIB+PSsY3reK0QN6YGaFDrFDXzn+K4UOQUREMpDuFvotwHFplB2yYokEL65+l5frtlNz7tUsrdtBn6oyEg4rN+/Ggb7V5Rw3uh/HjuhbtIn9vab3AOhR3qPAkYiISFd0mNDN7ATgRKDGzL6R8lIfIJrLwErJ5p0NPP7qJrbu2cf4mp48d8uVXD/n3v1JOxZP8Gb9HpbWbefpFfWs2LiLU48aQv8eFQWO/GBn3XUWAPNnzy9sICIi0iWdnRRXAfQimfh7pzx2AhfkNrTSsHrLHu5bXEdDLM65E4dz9vuH0/DO0hZb4GXRCBOG9uYzk0dy2lFD2LpnH/csXMuabe8VMHIREQmTDrfQ3X0BsMDM7nD3d/IUU8lYuWkX85ZvZGCvSj41aTg9Kjo+gmFmHDmsDyP7V/PgkvU8uGQdpx01lAlDe+cpYhERCat0j6FXmtkcoDZ1Gnf/eC6CKgV1777HvOUbGdKninMnDaeyLP0jEL2ryvnM5JE8/PIGHlu+kYj+PCgiIt2UbkL/HXAr8CsgnrtwSsP29/bx6Csb6FtdzrkTu5bMm1WWRzl30nAeeGkdjy3bRGNTnKpynZYgIiKZSTehx9z9FzmNpEQ0xRM88vIGcPjkxOFUdiMJl0cjfHLicH6/uI7X9uyjpndlFiPNzOxJswsdgoiIZCDdhP6wmX0FeABobC509205iaqIPbtqC1v37ONTk4bTLwtnqVcFW+qvAVv37KMxFs9oiz9blNBFREpTukdvLwb+FXgOWBw8FuUqqGL1ztY9LK3bwaRR/RgzsGfW5tu7qpyBvSqIxRM8vnwT7p61eXfVlve2sOW9LQVrX0REMpPWFrq7j811IMWuMRbniVc3MaBnBSeNH5j1+VeWRelXXc5bW/bwct0OJo7ql/U20nHBfcl/I+p/6CIipSXdS7/Oaqvc3edmN5zi9fyb29izL87ZE4dTFs3Naem9qsoYObAHf121hRH9q3PShoiIhFO6men4lMfJwHXAJ3MUU9HZvKuBpXXbef+IvgztU5XDloxPHDWEyrII85ZthKjunSMiIulJK6G7++Upjy8AHyB5Fbl2mdltZrbZzJallA0wsyfMbGXw3D/ltWvMbJWZrTCz0zPtULa5O0+/Xk9VeZQTc7CrvbUeFWWcemTyanJ9T5yR8/ZERCQcMt13/B5weCd17gDOaFX2LeApdz8ceCoYx8yOAmYARwfT/NzMiuJP2Ss27WLjzgY+fPigbv1FrSvGDurJkUN70/eDF/Dq+p15aVNEREpbusfQHwaaT72OAkcC93U0jbs/Y2a1rYrPBaYFw78G5gNXB+X3uHsj8LaZrQKmAn9PJ75cicUTPPfmVmp6V3Jkni/P+pEjali2ag3/9w9L+eNXTsrZcfvWvjzly3lpR0REsivdg7Q/SRmOAe+4e10G7Q1x9w0A7r7BzAYH5SOA51Pq1QVlBzGzS4FLAUaPHp1BCOlbWreDXQ0xTj1ySN5vd1pVHmXbE79gWa9vM+evb/GVaYflpd0Lj7kwL+2IiEh2pXsMfQHwOsk7rfUH9mU5jrayZZt/xnb3Oe4+xd2n1NTUZDmMAxqb4ry4ehtjBvZg9IDC3Bv8vTee48xjhnLTkyt5s353Xtpcu2Mta3eszUtbIiKSPWkldDObDiwEPgNMB14ws0xun7rJzIYF8xwGbA7K64BRKfVGAuszmH/WvLR2O42xBCeNH1TIMPi3c4+mujzK1b9/mUQi9xecmfnATGY+MDPn7YiISHale2D2O8Dx7n6xu88ieXz72gzae4jkVecInh9MKZ9hZpVmNpbkCXcLM5h/VjTG4ixZu53xNT0Lfn31wb2ruPbso1j0zrv8duGagsYiIiLFK92EHnH3zSnjWzub1szuJnlS2wQzqzOzzwE/BD5hZiuBTwTjuPtykifZvQrMAy5z94Ld1W1JsHU+tXZAoUJo4fzjRnDi+IH8aN7rbN7ZUOhwRESkCKWb0OeZ2WNmNtvMZgOPAn/qaAJ3v8jdh7l7ubuPdPf/dfet7n6Kux8ePG9LqX+9u4939wnu/ufMu9Q9VlHNS2u2M3ZQTwbn9CIy6TMzrv/0sTTGEvzbI68WOhwRESlCnW1lH2ZmJ7n7vwL/A7wfmEhyy3tOHuLLu97HnU1jLMEHxxbH1nmzsYN6cvnHDuPRlzfw9OubO59AREQOKZ1tod8E7AJw9/vd/Rvu/nWSW+c35Ta0/NvdGKPP1E9TO7AHQ4pk6zzVFz86nsMG9+K7f1zGe/tiOWnjmyd8k2+e8M2czFtERHKns4Re6+4vty5090VAbU4iKqC5f19NtLoPHxyX+0u8ZqKiLMINnz6Wddv38t9PrsxJG+dMOIdzJpyTk3mLiEjudJbQO9pMDdXtwPY0xvjlM2+x981FOb4BS/dMHTuAGceP4ld/ezsnl4VdsWUFK7asyPp8RUQktzpL6C+a2RdaFwZnrC/OTUiFsWlnA8P7VbP92d8WOpROfevM99G/RznXPPAK8Sz/N/2Lj3yRLz7yxazOU0REcq+zhH4lcImZzTez/wweC4DPA1/LeXR5NK6mF49c/mH2bXij0KF0ql+PCq49+yiWrt3OXS+8U+hwRESkCHSY0N19k7ufCPwbsDp4/Ju7n+DuG3MfXn7l+3rt3fHJicM5+fBB/HjeCjbpv+kiIoe8dK/l/rS73xI8/pLroKRzZsb3P3UMTfEE1z20vNDhiIhIgeXnnpySE2MG9uSKUw7nz8s28uSrmwodjoiIFFC6t0+VInXpR8bx0JL1/L+HlnPC+IH0rOzeKv3uR76bpchERCSftIVe4sqjEW447xjW79jLDX96rdvzO3XcqZw67tQsRCYiIvmkhB4Ck8cM4NKTx3HXC2t4ekX3Lgu7ZOMSlmxckp3AREQkb5TQQ+LrnziCCUN6c/XvX+bdPfsyns+V867kynlXZi8wERHJCyX0kKgqj3LjhRN59719XPvgskKHIyIieaaEHiJHD+/LlacewSMvb+DBJesKHY6IiOSREnrIfPEj4/jA6H5c+8dlbNixt9DhiIhIniihh0xZNMKN0ycRSzhX3P0STfFEoUMSEZE8UEIPobGDevKD847lxdXv8pPHu3bntBtOuYEbTrkhR5GJiEiu6MIyIXXupBG88PY2/mfBWxw3uj+nHz00relOHHVijiMTEZFc0BZ6iH3v7KN4/8i+fOPeJazYuCutaZ5b+xzPrX0ux5GJiEi2KaGHWFV5lDkzp9CzsozPz32RbWn8P/3bT32bbz/17TxEJyIi2aSEHnJD+1YxZ9YUNu1s5PO/fpG9++KFDklERHJACf0QMGlUP26eMYmX1m7n8rv/QUxnvouIhI4S+iHijGOG8e/nHsOTr23mqt8tJZ7wQockIiJZpLPcDyEzPzSGnXub+I/HVmBm/OQzE4lGrNBhiYhIFiihH2Iu+9hhuDs/efwN9sUT3Dh9IpVl0f2v33TGTYULTkREMlaQhG5mq4FdQByIufsUMxsA3AvUAquB6e7+biHiC7uvfvxwKsoi3PCn19m2ex//M2syfarKAZg0dFJhgxMRkYwU8hj6x9x9krtPCca/BTzl7ocDTwXjkiOXfmQ8N06fyIurt/Hpnz3Lm/W7AXjyrSd58q0nCxydiIh0VTGdFHcu8Otg+NfApwoXyqHhvONGMvdzU3n3vSbO/emz/PmVDXz/me/z/We+X+jQRESkiwqV0B143MwWm9mlQdkQd98AEDwPLlBsh5QTxw/i4cs/zLiannz5rn/wZv1unQEvIlKCCpXQT3L344AzgcvM7CPpTmhml5rZIjNbVF9fn7sIDyEj+lXz+y+dyGUfG8+W3Y0srdvOvGUbcFdiFxEpFQVJ6O6+PnjeDDwATAU2mdkwgOB5czvTznH3Ke4+paamJl8hh15FWYR/Pf19HD28L2WRCF/6zT+YddtClq3bUejQREQkDXk/y93MegIRd98VDJ8G/DvwEHAx8MPg+cF8xxZWo0aPoW7tmvQqz04+xV6Zw/y9M/jryi3sWfEsVav/xtqXnslZjCIi0j2F+NvaEOABM2tu/7fuPs/MXgTuM7PPAWuAzxQgtlCqW7uGG9O8L/rmvW8BMPj/jKMxFucf72xnadnJNE44ifN+/iyfmTKKf3r/sP1/cxMRkeKQ94Tu7m8BE9so3wqcku94pKXB1eP2D1eWRTlh/EAmj+nP9d/7Fjsv+jrX3P8K1z20nNOPHsq5k4Zz4vhBVFdEO5ijiIjkg64UJy0s3/YXAI4e8PH9ZRVlEXYtepAnFj7Ay3U7+P3iOh5aup6Hlq6noizCB8cO4KNH1HDy4TUcPrgXkRK5nGyXDkV0YOSo0axd804WIhIRyZwSurQwf/3tQMuE3szMmDiqHxNH9eO7Zx/Jwre3sWBFPfPfqOf7j74GvEbvyjImjurHB0b34/0j+zFhSG9G9q/en+SzkUSzlUC7ciiiI984bUK35yEi0l1K6JKRyrIoJx+e3Cr/LlD37nu88NY2Xlr7Li+t2c7P57+5///s1eVRDhvci8OH9GLnsOP56o8eoG9VOX2qyymPdv2PFkqgIiIHU0KXrBjZvwcjJ/fg/MkjAXhvX4zXNuxi5aZdvLFpNys37+LZVVvo/7FLeHjphv3TVZdH6VNdRp8gwfepKqNPdTm9K8voVVXW4sYxIiLSPiV0yYkeFWVMHtOfyWP6tyiPVvXim795lp17Y+xsaGLn3iZ2NDSxeVcjb9bvpvVF6iqiEXoFyX3/8/tPY/6KzQzvV83QvlX0riwj+NeEiMghSwld8irRuIdhfasZ1reN19zZ0xhjZ0OM3Q0xdjcmn3c1NrG7McbW3Y3s2Rdn4JlXMPv2F/dP17MiyrB+1QzrW8XQPlX7h5OPZNLvU6WkLyLhpoQuLfyfw39csLYjZvSuKqd3B/9xjyecb00/meeXvs6GHQ1s2LGXDTsa2LijgfU7GnhjUz2bdzXS+qq1PSuiDO1bRe3Anhw+pDcThvaifPBYYokEZZFiukeRiEhmlNClhf6Vw9p+wSJFsYUbjRjxnfVMqR3Qbp2meILNuxrZGCT7Ddsb9if/t7fs4ZmV9TTFneGX3MIv5r9JTe9KhvY5sHXft1oXzRGR0qOELi28tOVPAHxg0FktX/BEyfzFqzwaYUS/akb0q27z9aZ4gtVb9jBp2lmcccWP2LijgVc37GRpXfK69X2ryxnVv5rRA3owckAPqst1Yp6IFD8ldGnhuY13A20k9GKSxb0FHz5sEACJhLN1zz7Wb9/Lmm3v8cam3SxbvxMDRvSv5rCaXoyr6dnh4QARkUJSQpfSk4O9BZGIUdO7kprelUwc1Y9Ewtm0q4G3t+zhzc17mP9G8gI6Q/tU8b5hvTliSG9tuYtIUVFCF2lDJGLB2fjVnDh+ENv27OPN+t2s2LSL+SvqeeaNesYO6slRw/qA6aQ6ESk8JXSRNAzoWcGAngOYMqY/W3bv47UNO3l94y7erN/DiC/9L7c8tZIZU0dT07uy0KGKyCFKCV2kC8yad83XcNJhg3h7yx5+/9A/+M8n3uDmv6zkjGOGMfNDYzi+tn9R/CtARA4dSujSwsUT/rvQIZSMaMQ4bHAvNt/3PVZt3sVdz6/hd4vX8vDS9bxvaG9mnVDLpz4wnB4V+piJSO7p4J+00Kt8AL3K2/+Pt7RtfE0vvnfOUbzw7VP40fnHEjHj2w+8wodueIrrH32VNVvfK3SIIhJy2nSQFhZuvh+AqYPPK3AkpalHRRkXHj+a6VNGseidd7njudXc9uxqfvW3tznlfYO5+MRaPnzYIO2OF5GsU0KXFl7c/ACghN5dZsbxtQM4vnYAG3c0cNcL7/DbF9bw5GsLGV/Tk4tPrOW840bSq1IfQRHJDu1yF8mxoX2r+OZpE3jumo9z4/SJ9Kws43sPLueEG57iuoeW8/aWPYUOUURCQJsHIt2VwZXrKoZPYM9x53D7eydxx3Or8fXL+Z//O4uPva9G94AXkYwooYt0VzeuXLenMcYr63bw7K6hfOk3i+nXo5xz3j+c844bwaRR/XSsXUTSpoQuUkA9K8v40LiB3PflD/P0axu4/x/ruG/RWu58/h1G9q/mtKOGcvrRQ5hSO4BoRMldRNqnhF7MCnDL0i8cOSev7UnAE0ybMJhpEwazs6GJecs28tiyjfzmhXe47dm3GdCzgo8eUcMJ4wZywviBjBrQo9ARi0iRUUIvZgW4ZWlFtO1bjkr+9KkqZ/qUUUyfMordjTGeeaOex5dv5K8r63ngpXUAjBpQzfFjBnDMiL4cO7IvRw3rQ0+dMS9ySNM3gLTw7Ia7ADhp2GcLHIkA9Kos46xjh3HWscNwd1Zu3s1zq7bw3Jtb+duqLdwfJHgzqB3Yk9qBPRg7qBdjB/Vg1IAeDOlTxdA+VfTrUa7j8SIhp4QuLSzZOg9QQi9GZsYRQ5K3bp190lgANu9s4JV1O3hl3Q5WbtrN21v28Pxb29jbFG8xbUU0wqBeFfSpLqdPVTl9qsuC53J6VZZRVR6hqjxKZXmUyrJguIPnyrLI/rplEdOPhZCIJ5zGWJx9sQSNsQSxhJNIOAl34gkn4ZDw5HgicWDYHc4++2w2bVgPngBP4J6ARDCcSADJ8WR5HI834bGmZP0UI0eNZu2adwqzAEqcErpICRvcp4pT+lRxypFDABg1egx1a9cQ7TWQsr41RHsNDB4D2NazH5HKnslHVS8iVclhq+yBdeMWsJ6I47EmPL4v+Rzbh8f2QbyJRKzxQFn8wGvN9YntIxE7MB3xJjwewxMxPN4E8RgDBwzgvrvvoqLMqIhGKS8zyqMRKqIRyqMRyqNGWTRCNGJEDCJmRMz2j5fSj41EwmlKJIgnnKaY0xCL09AUp6EpETzHaYwFw7EEX/3a13l3526srBIrKydSVhEMVwSPciyaHCZanhwvq8BShqPlVfTq03d/As9U9MxrGJ7BdGZQFjGiZkSjxvZN6zj1xgVURCNUlkeC5+j+8aqyKNUVEarLo1SXR6mqiO4frq6IUtVcvr8skjIcpaosSiSkJ5gWXUI3szOA/waiwK/c/YcFDkmkZNStXdPl8y7cnXiwBRaLJ5+//y9ncNWcR/ePxxJOLJ4g7snheGp5kIBiiQNl8bjz8t//woTjP7q/vEX9ePNwegnkol8+n8niAJIJI2pGU9M+PBFPbjXiya1GT92C9ANblJ6Ag0JzysrKGDduHC3SQavckDqa+mMi4cnlG4sn9ve9KZ7Yv9ybEgm8i/k08qFZDGzV17KIURZJ/sApiySTZGrCLItEUoaN5x/5LV+44vIgeUapLE/ugakoi1AeiSSXXyT5IykS/EiKWnKvTCR4DeCcT57LJdf9HHfHg/66k3yQHG4uS6S851LfG/GE88LiV5lw8gdojCX27y3Yubcp2Gtw4MfN3uDR1WUGkGhqxGONyR+XTY14U2Pyx2cw7MFwj6pyvvz5f6Gy+QdBeWT/j4YeFWX7fzBUl5dRXXHgR0N1eZTyaP73XBVVQjezKPAz4BNAHfCimT3k7q8WNjKRHCvAPxr2N21GmRllEWg+ry62tY7Bvau6Nd+nrvp3rvlixz8u3JO7ceMpPwyax+PBrt6bvjaDvyx4hqa40xRL0BRPsC+eSI7Hg/FYMhnG9+8OduIpu4QT7tzwgx/yselfwAmSTHNywZO5PCXxtE4SHmT3l55+lGV1r6cuvPY71/o19+AHRQycZDJJxJO7nxOxA8Px2IGypuY9Go0pezeSez2SCWgf1931NGXRA0m8q/505RyufeLWLk/XlsMG9+r2PP505U38/NEb058gWk6kvDK5ZyLl+Ss33r3/x1NTPJH8MRX8MG1KeJuvNTX/2Aqet2/byi+efJVIRdc/C56I400NWCLG49ecw4Shvbs8j64qqoQOTAVWuftbAGZ2D3AuoIQu4VaAfzQUAzMjGmzlVbRzJerG9Ss46bCarLR38ne+3a3pn/jGf2RtPWVrPt3+d0OxvfeyEM83TpvA6Cz8tfMbp53BjY+vSO7FSjhNqT8AUn4YNLU1nkg+//Xhe+hbnZ97Y5hnsr8iR8zsAuAMd/98MD4T+KC7fzWlzqXApcHoBCCTNT8I2NLNcItJmPoTpr5AuPoTpr5AuPoTpr5AuPqTi76McfeDfuUW2xZ6W/uLWvzicPc5QLeufmJmi9x9SnfmUUzC1J8w9QXC1Z8w9QXC1Z8w9QXC1Z989qXY7rZWB4xKGR8JrC9QLCIiIiWj2BL6i8DhZjbWzCqAGcBDBY5JRESk6BXVLnd3j5nZV4HHSP5t7TZ3X56DpsJ2wfIw9SdMfYFw9SdMfYFw9SdMfYFw9SdvfSmqk+JEREQkM8W2y11EREQyoIQuIiISAodcQjezM8xshZmtMrNvFTqeZmY2ysyeNrPXzGy5mX0tKL/OzNaZ2ZLgcVbKNNcE/VhhZqenlE82s1eC12624BJkZlZpZvcG5S+YWW0O+7M6iGGJmS0KygaY2RNmtjJ47l8ifZmQsvyXmNlOM7uyVNaNmd1mZpvNbFlKWV7WhZldHLSx0swuzmF//sPMXjezl83sATPrF5TXmtnelHV0a8o0Be9PO33Jy/sqj+vm3pS+rDazJUF5sa+b9r6Ti/ezk7z84aHxIHmi3ZvAOKACWAocVei4gtiGAccFw72BN4CjgOuAq9qof1QQfyUwNuhXNHhtIXACyf/1/xk4Myj/CnBrMDwDuDeH/VkNDGpV9mPgW8Hwt4AflUJf2ngPbQTGlMq6AT4CHAcsy+e6AAYAbwXP/YPh/jnqz2lAWTD8o5T+1KbWazWfgvennb7k/H2Vz3XT6vX/BL5XIuumve/kov3sHGpb6PsvLevu+4DmS8sWnLtvcPd/BMO7gNeAER1Mci5wj7s3uvvbwCpgqpkNA/q4+989+c6YC3wqZZpfB8O/B05p/qWYJ6nt/7pVXKXSl1OAN929o/s7FlV/3P0ZYFsbMeZ6XZwOPOHu29z9XeAJ4Ixc9MfdH3f3WDD6PMlrWLSrWPrTzrppT0mum2ZBu9OBuzuaR7H0p4Pv5KL97BxqCX0EsDZlvI6Ok2ZBBLtdPgC8EBR91ZK7Em9L2b3TXl9GBMOty1tME3z57YAWN2vKJgceN7PFlrxcL8AQd98QtL8BGNw6rlYxF0tfUs2g5RdSKa4byM+6KNTn7V9IbgU1G2tmL5nZAjM7OSgr9v7k+n1ViHVzMrDJ3VemlJXEumn1nVy0n51DLaF3emnZQjOzXsAfgCvdfSfwC2A8MAnYQHKXFbTfl476mM/+n+TuxwFnApeZ2Uc6qFvsfUk2mLzY0SeB3wVFpbpuOpLN2Auxjr4DxIC7gqINwGh3/wDwDeC3Ztank9gK3Z98vK8K8X67iJY/hkti3bTxndxu1Qxiy2p/DrWEXtSXljWzcpJvnLvc/X4Ad9/k7nF3TwC/JHnYANrvSx0tdzem9nH/NGZWBvQl/d19XeLu64PnzcADQdybgt1PzbvVNpdCX1KcCfzD3TdB6a6bQD7WRV4/b8GJQ2cDnw12bRLs/twaDC8meVzziGLuT57eV/leN2XAecC9zWWlsG7a+k6mmD876Z4gEIYHySvjvUXyhIXmk+KOLnRcQWxG8tjKTa3Kh6UMf53kMRqAo2l5AsZbHDgB40XgQxw4AeOsoPwyWp6AcV+O+tIT6J0y/BzJ4z//QcuTSX5c7H1p1a97gEtKcd3Q6gSkfKwLkif0vE3ypJ7+wfCAHPXnDJK3Wa5pVa8mJf5xwLrmGIqlP230Jefvq3yum5T1s6CU1g3tfycX7Wcnp1+AxfgAziJ5tuKbwHcKHU9KXB8muUvlZWBJ8DgLuBN4JSh/qNWH/TtBP1YQnDUZlE8BlgWv/ZQDVwSsIrm7eBXJsy7H5agv44I39lJgefNyJnls6ClgZfA8oNj7khJHD2Ar0DelrCTWDcndnBuAJpK//D+Xr3VB8nj2quBxSQ77s4rkMcfmz07zl+T5wXtwKfAP4Jxi6k87fcnL+ypf6yYovwP4Uqu6xb5u2vtOLtrPji79KiIiEgKH2jF0ERGRUFJCFxERCQEldBERkRBQQhcREQkBJXQREZEQUEIXEREJASV0kSJlZp82Mzez92V5vrPMbFlwS8hXzeyqbsyr1oJbZZrZFDO7ORieZmYndjLtdW21bWbPdTGGL5nZrGB4tpkN78r0ImGhhC5SvC4C/kbyClJZYWZnAlcCp7n70SRvdbmjjXplXZ23uy9y9yuC0WlAhwm9g/l0aTp3v9Xd5wajswEldDkkKaGLFKHghhAnkbxy2AwzO9PM7kt5fZqZPRwMf87M3jCz+Wb2SzP7aQezvobkvbabr7Xf4O6/DOYz38xuMLMFwNfMbHJwF6zFZvZYyvWrJ5vZUjP7O8lLV6bG9EhwZ6ovAV83syUpd9FKt++7U+a3wMzuC/r3QzP7rJktNLNXzGx8UO86M7vKzC4geUWuu4J2q7vSrkipU0IXKU6fAua5+xskb9awFfiQmfUMXr8QuDfYvXwtyetEfwLobPf8McDiDl7v5+4fBW4GbgEucPfJwG3A9UGd24Er3P2Etmbg7quBW4H/cvdJ7v7XTmLqyETga8CxwEzgCHefCvwKuLxVu78HFpG8Ocskd9/bjXZFSo4SukhxuojkzWAInj8DzAPOCXaH/xPwIMk7cS1w923u3sSBW7tmqvluWBNIJv8nzGwJ8F1gpJn1JZn0FwT17uxme5150d03uHsjyetgPx6Uv0LyJiAiEujycTIRyS0zGwh8HDjGzByIkrxJxCUkd3FvI5nodplZW/dN7shyYDLwl3Ze39McBrC89Va4mfUjv/dpb0wZTqSMJ9D3l0gL2kIXKT4XAHPdfYy717r7KJK3T4yRPIntCxzYkl4IfNTM+gdb7ud3Mu8fAD82s6EAZlZpZle0UW8FUGNmJwT1ys3saHffDuwwsw8H9T7bTju7gN7pdDbLCtWuSMEpoYsUn4uAB1qV/YHk2e6PAGcGz7j7OuAG4AXgSZL3BD/orPVm7v4n4GfAk2a2nOTx9IO2dN19H8kfFj8ys6Ukbx3ZfPb5JcDPgpPi2jtO/TDw6TROivuumdU1Pzqol647gFt1UpwcinT7VJESZ2a93H13sIX+AHCbu7f+QSAiIactdJHSd11w4toykrvm/1jQaESkILSFLhJCZvYdkmfGp/qdu1/fVv1DJRaRMFNCFxERCQHtchcREQkBJXQREZEQUEIXEREJASV0ERGREPj/ELHtrUnm6O4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# selecting all numerical columns\\nnum_col = data.select_dtypes(include=np.number).columns.tolist()\\n\\nfor item in num_col:\\n    histogram_boxplot(data, item, kde=True, figsize=(8, 4))\";\n",
       "                var nbb_formatted_code = \"# selecting all numerical columns\\nnum_col = data.select_dtypes(include=np.number).columns.tolist()\\n\\nfor item in num_col:\\n    histogram_boxplot(data, item, kde=True, figsize=(8, 4))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# selecting all numerical columns\n",
    "num_col = data.select_dtypes(include=np.number).columns.tolist()\n",
    "\n",
    "for item in num_col:\n",
    "    histogram_boxplot(data, item, kde=True, figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5eb113a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# function to create labeled barplots\\n\\n\\ndef labeled_barplot(data, feature, perc=False, n=None):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    feature: dataframe column\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    \\\"\\\"\\\"\\n\\n    total = len(data[feature])  # length of the column\\n    count = data[feature].nunique()\\n    if n is None:\\n        plt.figure(figsize=(count + 1, 5))\\n    else:\\n        plt.figure(figsize=(n + 1, 5))\\n\\n    plt.xticks(rotation=90, fontsize=15)\\n    ax = sns.countplot(\\n        data=data,\\n        x=feature,\\n        palette=\\\"Paired\\\",\\n        order=data[feature].value_counts().index[:n].sort_values(),\\n    )\\n\\n    for p in ax.patches:\\n        if perc == True:\\n            label = \\\"{:.1f}%\\\".format(\\n                100 * p.get_height() / total\\n            )  # percentage of each class of the category\\n        else:\\n            label = p.get_height()  # count of each level of the category\\n\\n        x = p.get_x() + p.get_width() / 2  # width of the plot\\n        y = p.get_height()  # height of the plot\\n\\n        ax.annotate(\\n            label,\\n            (x, y),\\n            ha=\\\"center\\\",\\n            va=\\\"center\\\",\\n            size=12,\\n            xytext=(0, 5),\\n            textcoords=\\\"offset points\\\",\\n        )  # annotate the percentage\\n\\n    plt.show()  # show the plot\";\n",
       "                var nbb_formatted_code = \"# function to create labeled barplots\\n\\n\\ndef labeled_barplot(data, feature, perc=False, n=None):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    feature: dataframe column\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    \\\"\\\"\\\"\\n\\n    total = len(data[feature])  # length of the column\\n    count = data[feature].nunique()\\n    if n is None:\\n        plt.figure(figsize=(count + 1, 5))\\n    else:\\n        plt.figure(figsize=(n + 1, 5))\\n\\n    plt.xticks(rotation=90, fontsize=15)\\n    ax = sns.countplot(\\n        data=data,\\n        x=feature,\\n        palette=\\\"Paired\\\",\\n        order=data[feature].value_counts().index[:n].sort_values(),\\n    )\\n\\n    for p in ax.patches:\\n        if perc == True:\\n            label = \\\"{:.1f}%\\\".format(\\n                100 * p.get_height() / total\\n            )  # percentage of each class of the category\\n        else:\\n            label = p.get_height()  # count of each level of the category\\n\\n        x = p.get_x() + p.get_width() / 2  # width of the plot\\n        y = p.get_height()  # height of the plot\\n\\n        ax.annotate(\\n            label,\\n            (x, y),\\n            ha=\\\"center\\\",\\n            va=\\\"center\\\",\\n            size=12,\\n            xytext=(0, 5),\\n            textcoords=\\\"offset points\\\",\\n        )  # annotate the percentage\\n\\n    plt.show()  # show the plot\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to create labeled barplots\n",
    "\n",
    "\n",
    "def labeled_barplot(data, feature, perc=False, n=None):\n",
    "    \"\"\"\n",
    "    Barplot with percentage at the top\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    perc: whether to display percentages instead of count (default is False)\n",
    "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
    "    \"\"\"\n",
    "\n",
    "    total = len(data[feature])  # length of the column\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 1, 5))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 1, 5))\n",
    "\n",
    "    plt.xticks(rotation=90, fontsize=15)\n",
    "    ax = sns.countplot(\n",
    "        data=data,\n",
    "        x=feature,\n",
    "        palette=\"Paired\",\n",
    "        order=data[feature].value_counts().index[:n].sort_values(),\n",
    "    )\n",
    "\n",
    "    for p in ax.patches:\n",
    "        if perc == True:\n",
    "            label = \"{:.1f}%\".format(\n",
    "                100 * p.get_height() / total\n",
    "            )  # percentage of each class of the category\n",
    "        else:\n",
    "            label = p.get_height()  # count of each level of the category\n",
    "\n",
    "        x = p.get_x() + p.get_width() / 2  # width of the plot\n",
    "        y = p.get_height()  # height of the plot\n",
    "\n",
    "        ax.annotate(\n",
    "            label,\n",
    "            (x, y),\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            size=12,\n",
    "            xytext=(0, 5),\n",
    "            textcoords=\"offset points\",\n",
    "        )  # annotate the percentage\n",
    "\n",
    "    plt.show()  # show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bd0f9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"# function to create labeled barplots\\n\\n\\ndef labeled_barplot_scaled(DataScaled, feature, perc=False, n=None):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    feature: dataframe column\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    \\\"\\\"\\\"\\n\\n    total = len(data[feature])  # length of the column\\n    count = data[feature].nunique()\\n    if n is None:\\n        plt.figure(figsize=(count + 1, 5))\\n    else:\\n        plt.figure(figsize=(n + 1, 5))\\n\\n    plt.xticks(rotation=90, fontsize=15)\\n    ax = sns.countplot(\\n        DataScaled=DataScaled,\\n        x=feature,\\n        palette=\\\"Paired\\\",\\n        order=data[feature].value_counts().index[:n].sort_values(),\\n    )\\n\\n    for p in ax.patches:\\n        if perc == True:\\n            label = \\\"{:.1f}%\\\".format(\\n                100 * p.get_height() / total\\n            )  # percentage of each class of the category\\n        else:\\n            label = p.get_height()  # count of each level of the category\\n\\n        x = p.get_x() + p.get_width() / 2  # width of the plot\\n        y = p.get_height()  # height of the plot\\n\\n        ax.annotate(\\n            label,\\n            (x, y),\\n            ha=\\\"center\\\",\\n            va=\\\"center\\\",\\n            size=12,\\n            xytext=(0, 5),\\n            textcoords=\\\"offset points\\\",\\n        )  # annotate the percentage\\n\\n    plt.show()  # show the plot\";\n",
       "                var nbb_formatted_code = \"# function to create labeled barplots\\n\\n\\ndef labeled_barplot_scaled(DataScaled, feature, perc=False, n=None):\\n    \\\"\\\"\\\"\\n    Barplot with percentage at the top\\n\\n    data: dataframe\\n    feature: dataframe column\\n    perc: whether to display percentages instead of count (default is False)\\n    n: displays the top n category levels (default is None, i.e., display all levels)\\n    \\\"\\\"\\\"\\n\\n    total = len(data[feature])  # length of the column\\n    count = data[feature].nunique()\\n    if n is None:\\n        plt.figure(figsize=(count + 1, 5))\\n    else:\\n        plt.figure(figsize=(n + 1, 5))\\n\\n    plt.xticks(rotation=90, fontsize=15)\\n    ax = sns.countplot(\\n        DataScaled=DataScaled,\\n        x=feature,\\n        palette=\\\"Paired\\\",\\n        order=data[feature].value_counts().index[:n].sort_values(),\\n    )\\n\\n    for p in ax.patches:\\n        if perc == True:\\n            label = \\\"{:.1f}%\\\".format(\\n                100 * p.get_height() / total\\n            )  # percentage of each class of the category\\n        else:\\n            label = p.get_height()  # count of each level of the category\\n\\n        x = p.get_x() + p.get_width() / 2  # width of the plot\\n        y = p.get_height()  # height of the plot\\n\\n        ax.annotate(\\n            label,\\n            (x, y),\\n            ha=\\\"center\\\",\\n            va=\\\"center\\\",\\n            size=12,\\n            xytext=(0, 5),\\n            textcoords=\\\"offset points\\\",\\n        )  # annotate the percentage\\n\\n    plt.show()  # show the plot\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to create labeled barplots\n",
    "\n",
    "\n",
    "def labeled_barplot_scaled(DataScaled, feature, perc=False, n=None):\n",
    "    \"\"\"\n",
    "    Barplot with percentage at the top\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    perc: whether to display percentages instead of count (default is False)\n",
    "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
    "    \"\"\"\n",
    "\n",
    "    total = len(data[feature])  # length of the column\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 1, 5))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 1, 5))\n",
    "\n",
    "    plt.xticks(rotation=90, fontsize=15)\n",
    "    ax = sns.countplot(\n",
    "        DataScaled=DataScaled,\n",
    "        x=feature,\n",
    "        palette=\"Paired\",\n",
    "        order=data[feature].value_counts().index[:n].sort_values(),\n",
    "    )\n",
    "\n",
    "    for p in ax.patches:\n",
    "        if perc == True:\n",
    "            label = \"{:.1f}%\".format(\n",
    "                100 * p.get_height() / total\n",
    "            )  # percentage of each class of the category\n",
    "        else:\n",
    "            label = p.get_height()  # count of each level of the category\n",
    "\n",
    "        x = p.get_x() + p.get_width() / 2  # width of the plot\n",
    "        y = p.get_height()  # height of the plot\n",
    "\n",
    "        ax.annotate(\n",
    "            label,\n",
    "            (x, y),\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            size=12,\n",
    "            xytext=(0, 5),\n",
    "            textcoords=\"offset points\",\n",
    "        )  # annotate the percentage\n",
    "\n",
    "    plt.show()  # show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0ff9ba46",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 7992x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"for item in num_col:\\n    labeled_barplot(data, item, perc=True)\";\n",
       "                var nbb_formatted_code = \"for item in num_col:\\n    labeled_barplot(data, item, perc=True)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in num_col:\n",
    "    labeled_barplot(data, item, perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd9b324",
   "metadata": {},
   "source": [
    "#### Scaleing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a99d3e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"# Convert features to float\\ndata = data.astype(\\\"float64\\\")\";\n",
       "                var nbb_formatted_code = \"# Convert features to float\\ndata = data.astype(\\\"float64\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert features to float\n",
    "data = data.astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3a46e6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 660 entries, 0 to 659\n",
      "Data columns (total 5 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Avg_Credit_Limit     660 non-null    float64\n",
      " 1   Total_Credit_Cards   660 non-null    float64\n",
      " 2   Total_visits_bank    660 non-null    float64\n",
      " 3   Total_visits_online  660 non-null    float64\n",
      " 4   Total_calls_made     660 non-null    float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 25.9 KB\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"data.info()\";\n",
       "                var nbb_formatted_code = \"data.info()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7dc9fd",
   "metadata": {},
   "source": [
    "* The memory usage was not increased when converting to a float. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf25508",
   "metadata": {},
   "source": [
    "I want to better understand my quartiles before scaling down the whole dataframe so it can be used as a reference when interpretating the scaled data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "24fad3b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg_Credit_Limit</th>\n",
       "      <td>660.0</td>\n",
       "      <td>34574.242424</td>\n",
       "      <td>37625.487804</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <td>660.0</td>\n",
       "      <td>4.706061</td>\n",
       "      <td>2.167835</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <td>660.0</td>\n",
       "      <td>2.403030</td>\n",
       "      <td>1.631813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_online</th>\n",
       "      <td>660.0</td>\n",
       "      <td>2.606061</td>\n",
       "      <td>2.935724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_calls_made</th>\n",
       "      <td>660.0</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>2.865317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</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     660.0  34574.242424  37625.487804  3000.0  10000.0   \n",
       "Total_Credit_Cards   660.0      4.706061      2.167835     1.0      3.0   \n",
       "Total_visits_bank    660.0      2.403030      1.631813     0.0      1.0   \n",
       "Total_visits_online  660.0      2.606061      2.935724     0.0      1.0   \n",
       "Total_calls_made     660.0      3.583333      2.865317     0.0      1.0   \n",
       "\n",
       "                         50%      75%       max  \n",
       "Avg_Credit_Limit     18000.0  48000.0  200000.0  \n",
       "Total_Credit_Cards       5.0      6.0      10.0  \n",
       "Total_visits_bank        2.0      4.0       5.0  \n",
       "Total_visits_online      2.0      4.0      15.0  \n",
       "Total_calls_made         3.0      5.0      10.0  "
      ]
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     "metadata": {},
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   "execution_count": 21,
   "id": "1d3ca88c",
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "    <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",
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       "  </tbody>\n",
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      "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",
       "   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  "
      ]
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     "execution_count": 21,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 22,
   "id": "3e700ce2",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>-1.251537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.410293</td>\n",
       "      <td>-0.787585</td>\n",
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       "      <td>2.520519</td>\n",
       "      <td>1.891859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.410293</td>\n",
       "      <td>1.058973</td>\n",
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       "      <td>0.134290</td>\n",
       "      <td>0.145528</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.121665</td>\n",
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       "      <td>-0.547490</td>\n",
       "      <td>0.145528</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.740187</td>\n",
       "      <td>0.597334</td>\n",
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       "      <td>3.202298</td>\n",
       "      <td>-0.203739</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.387644</td>\n",
       "      <td>-0.787585</td>\n",
       "      <td>-1.473731</td>\n",
       "      <td>-0.547490</td>\n",
       "      <td>1.542593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.740187</td>\n",
       "      <td>0.135694</td>\n",
       "      <td>-1.473731</td>\n",
       "      <td>2.861408</td>\n",
       "      <td>-0.553005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.520633</td>\n",
       "      <td>-0.787585</td>\n",
       "      <td>-1.473731</td>\n",
       "      <td>-0.547490</td>\n",
       "      <td>-0.902271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.786612</td>\n",
       "      <td>-1.249225</td>\n",
       "      <td>-1.473731</td>\n",
       "      <td>-0.206600</td>\n",
       "      <td>-0.553005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.839808</td>\n",
       "      <td>-0.325946</td>\n",
       "      <td>-1.473731</td>\n",
       "      <td>-0.547490</td>\n",
       "      <td>1.193326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0          1.740187           -1.249225          -0.860451   \n",
       "1          0.410293           -0.787585          -1.473731   \n",
       "2          0.410293            1.058973          -0.860451   \n",
       "3         -0.121665            0.135694          -0.860451   \n",
       "4          1.740187            0.597334          -1.473731   \n",
       "5         -0.387644           -0.787585          -1.473731   \n",
       "6          1.740187            0.135694          -1.473731   \n",
       "7         -0.520633           -0.787585          -1.473731   \n",
       "8         -0.786612           -1.249225          -1.473731   \n",
       "9         -0.839808           -0.325946          -1.473731   \n",
       "\n",
       "   Total_visits_online  Total_calls_made  \n",
       "0            -0.547490         -1.251537  \n",
       "1             2.520519          1.891859  \n",
       "2             0.134290          0.145528  \n",
       "3            -0.547490          0.145528  \n",
       "4             3.202298         -0.203739  \n",
       "5            -0.547490          1.542593  \n",
       "6             2.861408         -0.553005  \n",
       "7            -0.547490         -0.902271  \n",
       "8            -0.206600         -0.553005  \n",
       "9            -0.547490          1.193326  "
      ]
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     "execution_count": 22,
     "metadata": {},
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       "            setTimeout(function() {\n",
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   "metadata": {},
   "source": [
    "#### Bivariate analysis Using Scaled Dataframe"
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  {
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   "execution_count": 23,
   "id": "af06c29f",
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   "outputs": [
    {
     "data": {
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     "execution_count": 23,
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# importing seaborn for statistical plots\\nsns.pairplot(DataScaled, height=2, aspect=2, diag_kind=\\\"kde\\\")\";\n",
       "                var nbb_formatted_code = \"# importing seaborn for statistical plots\\nsns.pairplot(DataScaled, height=2, aspect=2, diag_kind=\\\"kde\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing seaborn for statistical plots\n",
    "sns.pairplot(DataScaled, height=2, aspect=2, diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b839ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"# heat map using scaled data.\\nplt.figure(figsize=(15, 7))\\nsns.heatmap(DataScaled.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# heat map using scaled data.\\nplt.figure(figsize=(15, 7))\\nsns.heatmap(DataScaled.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heat map using scaled data.\n",
    "plt.figure(figsize=(15, 7))\n",
    "sns.heatmap(DataScaled.corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a7063360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"# heat map using unscaled data\\nplt.figure(figsize=(15, 7))\\nsns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# heat map using unscaled data\\nplt.figure(figsize=(15, 7))\\nsns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, fmt=\\\".2f\\\", cmap=\\\"Spectral\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heat map using unscaled data\n",
    "plt.figure(figsize=(15, 7))\n",
    "sns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9f0c66",
   "metadata": {},
   "source": [
    "* According to the heatmaps, Scaling did not change the correlation of any features\n",
    "* Total Credit Card limit has a high correlation with Total Credit Cards and Total Visits Online. However, Total Credit Cards and Total Visits Online have a low correlation with eachother. \n",
    "* Total Cards has a high negative relationship with total calls made and a positive correlation with visiting the bank. \n",
    "* Total visits online has high negative correlation with visiting the bank and a weak positive correlation with total calls. \n",
    "* Total calls made has a negative correlation with Credit Card Limit and a high negative correlation with total cards and total bank visits. It only has a positive relationship with visits online. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6d016b",
   "metadata": {},
   "source": [
    "### EDA Summary: \n",
    "The dataframe was scaled because money is a much bigger scale than customer service and card ownership features. \n",
    "No outliers where removed. There were no duplicates or nulls. \n",
    "\n",
    "* Happy customers with more credit cards have a higher credit card limit and call less often. \n",
    "* The more a customer calls the less likely they own a card. \n",
    "* Customers with high credit card limits do most of their banking online and are also less likely to call and visit the bank. \n",
    "* Credit card limit has a higher correlation with online visits than the amount of credit cards has.\n",
    "\n",
    "It it is safe to assume the most unsatisfied customers call more often. \n",
    "\n",
    "* People that vist the bank more often are more likely to own more credit cards. \n",
    "* Customers with more credit cards that have lower credit limits perfer to go to the bank for service. \n",
    "\n",
    "Upselling at the bank is more succesful than selling online.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e525f41e",
   "metadata": {},
   "source": [
    "### K-Means Clustering and Cluster Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c895abe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Selecting k with the Elbow Method')"
      ]
     },
     "execution_count": 26,
     "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"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"# Finding optimal no. of clusters\\nfrom scipy.spatial.distance import cdist\\n\\nclusters = range(1, 10)\\nmeanDistortions = []\\n\\nfor k in clusters:\\n    model = KMeans(n_clusters=k)\\n    model.fit(DataScaled)\\n    prediction = model.predict(DataScaled)\\n    meanDistortions.append(\\n        sum(np.min(cdist(DataScaled, model.cluster_centers_, \\\"euclidean\\\"), axis=1))\\n        / DataScaled.shape[0]\\n    )\\n\\n\\nplt.plot(clusters, meanDistortions, \\\"bx-\\\")\\nplt.xlabel(\\\"k\\\")\\nplt.ylabel(\\\"Average distortion\\\")\\nplt.title(\\\"Selecting k with the Elbow Method\\\")\";\n",
       "                var nbb_formatted_code = \"# Finding optimal no. of clusters\\nfrom scipy.spatial.distance import cdist\\n\\nclusters = range(1, 10)\\nmeanDistortions = []\\n\\nfor k in clusters:\\n    model = KMeans(n_clusters=k)\\n    model.fit(DataScaled)\\n    prediction = model.predict(DataScaled)\\n    meanDistortions.append(\\n        sum(np.min(cdist(DataScaled, model.cluster_centers_, \\\"euclidean\\\"), axis=1))\\n        / DataScaled.shape[0]\\n    )\\n\\n\\nplt.plot(clusters, meanDistortions, \\\"bx-\\\")\\nplt.xlabel(\\\"k\\\")\\nplt.ylabel(\\\"Average distortion\\\")\\nplt.title(\\\"Selecting k with the Elbow Method\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding optimal no. of clusters\n",
    "from scipy.spatial.distance import cdist\n",
    "\n",
    "clusters = range(1, 10)\n",
    "meanDistortions = []\n",
    "\n",
    "for k in clusters:\n",
    "    model = KMeans(n_clusters=k)\n",
    "    model.fit(DataScaled)\n",
    "    prediction = model.predict(DataScaled)\n",
    "    meanDistortions.append(\n",
    "        sum(np.min(cdist(DataScaled, model.cluster_centers_, \"euclidean\"), axis=1))\n",
    "        / DataScaled.shape[0]\n",
    "    )\n",
    "\n",
    "\n",
    "plt.plot(clusters, meanDistortions, \"bx-\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Average distortion\")\n",
    "plt.title(\"Selecting k with the Elbow Method\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d9a647",
   "metadata": {},
   "source": [
    "* 3 is the correct amount of clusters based off the elbow curve above. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340b0eca",
   "metadata": {},
   "source": [
    "#### Silhouette Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c57ed2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, silhouette score is 0.41842496663215445\n",
      "For n_clusters = 3, silhouette score is 0.5157182558881063\n",
      "For n_clusters = 4, silhouette score is 0.3556670619372605\n",
      "For n_clusters = 5, silhouette score is 0.2723911235693706\n",
      "For n_clusters = 6, silhouette score is 0.2555743483456167\n",
      "For n_clusters = 7, silhouette score is 0.24857211535274673\n",
      "For n_clusters = 8, silhouette score is 0.22756437271245886\n",
      "For n_clusters = 9, silhouette score is 0.22440496174070906\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x216cb466850>]"
      ]
     },
     "execution_count": 27,
     "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"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"sil_score = []\\ncluster_list = list(range(2, 10))\\nfor n_clusters in cluster_list:\\n    clusterer = KMeans(n_clusters=n_clusters)\\n    preds = clusterer.fit_predict((DataScaled))\\n    # centers = clusterer.cluster_centers_\\n    score = silhouette_score(DataScaled, preds)\\n    sil_score.append(score)\\n    print(\\\"For n_clusters = {}, silhouette score is {}\\\".format(n_clusters, score))\\n\\nplt.plot(cluster_list, sil_score)\";\n",
       "                var nbb_formatted_code = \"sil_score = []\\ncluster_list = list(range(2, 10))\\nfor n_clusters in cluster_list:\\n    clusterer = KMeans(n_clusters=n_clusters)\\n    preds = clusterer.fit_predict((DataScaled))\\n    # centers = clusterer.cluster_centers_\\n    score = silhouette_score(DataScaled, preds)\\n    sil_score.append(score)\\n    print(\\\"For n_clusters = {}, silhouette score is {}\\\".format(n_clusters, score))\\n\\nplt.plot(cluster_list, sil_score)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sil_score = []\n",
    "cluster_list = list(range(2, 10))\n",
    "for n_clusters in cluster_list:\n",
    "    clusterer = KMeans(n_clusters=n_clusters)\n",
    "    preds = clusterer.fit_predict((DataScaled))\n",
    "    # centers = clusterer.cluster_centers_\n",
    "    score = silhouette_score(DataScaled, preds)\n",
    "    sil_score.append(score)\n",
    "    print(\"For n_clusters = {}, silhouette score is {}\".format(n_clusters, score))\n",
    "\n",
    "plt.plot(cluster_list, sil_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd0929d",
   "metadata": {},
   "source": [
    "* From the silhouette scores, it seems that 3 is a good value of k because it is the highest point before dipping on the graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7859a892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"kmeans = KMeans(n_clusters=3, random_state=0)\\nkmeans.fit(DataScaled)\";\n",
       "                var nbb_formatted_code = \"kmeans = KMeans(n_clusters=3, random_state=0)\\nkmeans.fit(DataScaled)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=0)\n",
    "kmeans.fit(DataScaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf830cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"# adding kmeans cluster labels to the original dataframe\\ndata[\\\"K_means_segments\\\"] = kmeans.labels_\";\n",
       "                var nbb_formatted_code = \"# adding kmeans cluster labels to the original dataframe\\ndata[\\\"K_means_segments\\\"] = kmeans.labels_\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adding kmeans cluster labels to the original dataframe\n",
    "data[\"K_means_segments\"] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82b75ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"cluster_profile = data.groupby(\\\"K_means_segments\\\").mean()\";\n",
       "                var nbb_formatted_code = \"cluster_profile = data.groupby(\\\"K_means_segments\\\").mean()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_profile = data.groupby(\"K_means_segments\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "97429fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"cluster_profile[\\\"count_in_each_segment\\\"] = (\\n    data.groupby(\\\"K_means_segments\\\")[\\\"Total_Credit_Cards\\\"].count().values\\n)\";\n",
       "                var nbb_formatted_code = \"cluster_profile[\\\"count_in_each_segment\\\"] = (\\n    data.groupby(\\\"K_means_segments\\\")[\\\"Total_Credit_Cards\\\"].count().values\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_profile[\"count_in_each_segment\"] = (\n",
    "    data.groupby(\"K_means_segments\")[\"Total_Credit_Cards\"].count().values\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a35250a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_71ada_row0_col2,#T_71ada_row0_col5,#T_71ada_row1_col4,#T_71ada_row2_col0,#T_71ada_row2_col1,#T_71ada_row2_col3{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_71ada_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Avg_Credit_Limit</th>        <th class=\"col_heading level0 col1\" >Total_Credit_Cards</th>        <th class=\"col_heading level0 col2\" >Total_visits_bank</th>        <th class=\"col_heading level0 col3\" >Total_visits_online</th>        <th class=\"col_heading level0 col4\" >Total_calls_made</th>        <th class=\"col_heading level0 col5\" >count_in_each_segment</th>    </tr>    <tr>        <th class=\"index_name level0\" >K_means_segments</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_71ada_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_71ada_row0_col0\" class=\"data row0 col0\" >33782.383420</td>\n",
       "                        <td id=\"T_71ada_row0_col1\" class=\"data row0 col1\" >5.515544</td>\n",
       "                        <td id=\"T_71ada_row0_col2\" class=\"data row0 col2\" >3.489637</td>\n",
       "                        <td id=\"T_71ada_row0_col3\" class=\"data row0 col3\" >0.981865</td>\n",
       "                        <td id=\"T_71ada_row0_col4\" class=\"data row0 col4\" >2.000000</td>\n",
       "                        <td id=\"T_71ada_row0_col5\" class=\"data row0 col5\" >386</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_71ada_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_71ada_row1_col0\" class=\"data row1 col0\" >12174.107143</td>\n",
       "                        <td id=\"T_71ada_row1_col1\" class=\"data row1 col1\" >2.410714</td>\n",
       "                        <td id=\"T_71ada_row1_col2\" class=\"data row1 col2\" >0.933036</td>\n",
       "                        <td id=\"T_71ada_row1_col3\" class=\"data row1 col3\" >3.553571</td>\n",
       "                        <td id=\"T_71ada_row1_col4\" class=\"data row1 col4\" >6.870536</td>\n",
       "                        <td id=\"T_71ada_row1_col5\" class=\"data row1 col5\" >224</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_71ada_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_71ada_row2_col0\" class=\"data row2 col0\" >141040.000000</td>\n",
       "                        <td id=\"T_71ada_row2_col1\" class=\"data row2 col1\" >8.740000</td>\n",
       "                        <td id=\"T_71ada_row2_col2\" class=\"data row2 col2\" >0.600000</td>\n",
       "                        <td id=\"T_71ada_row2_col3\" class=\"data row2 col3\" >10.900000</td>\n",
       "                        <td id=\"T_71ada_row2_col4\" class=\"data row2 col4\" >1.080000</td>\n",
       "                        <td id=\"T_71ada_row2_col5\" class=\"data row2 col5\" >50</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x216c983d3d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"# let's display cluster profiles\\ncluster_profile.style.highlight_max(color=\\\"lightgreen\\\", axis=0)\";\n",
       "                var nbb_formatted_code = \"# let's display cluster profiles\\ncluster_profile.style.highlight_max(color=\\\"lightgreen\\\", axis=0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's display cluster profiles\n",
    "cluster_profile.style.highlight_max(color=\"lightgreen\", axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "330f42e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
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  {
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   "id": "84f49c8b",
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    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avg_Credit_Limit</th>\n",
       "      <td>660.0</td>\n",
       "      <td>34574.242424</td>\n",
       "      <td>37625.487804</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <td>660.0</td>\n",
       "      <td>4.706061</td>\n",
       "      <td>2.167835</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <td>660.0</td>\n",
       "      <td>2.403030</td>\n",
       "      <td>1.631813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_visits_online</th>\n",
       "      <td>660.0</td>\n",
       "      <td>2.606061</td>\n",
       "      <td>2.935724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_calls_made</th>\n",
       "      <td>660.0</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>2.865317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K_means_segments</th>\n",
       "      <td>660.0</td>\n",
       "      <td>0.490909</td>\n",
       "      <td>0.634068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean           std     min      25%  \\\n",
       "Avg_Credit_Limit     660.0  34574.242424  37625.487804  3000.0  10000.0   \n",
       "Total_Credit_Cards   660.0      4.706061      2.167835     1.0      3.0   \n",
       "Total_visits_bank    660.0      2.403030      1.631813     0.0      1.0   \n",
       "Total_visits_online  660.0      2.606061      2.935724     0.0      1.0   \n",
       "Total_calls_made     660.0      3.583333      2.865317     0.0      1.0   \n",
       "K_means_segments     660.0      0.490909      0.634068     0.0      0.0   \n",
       "\n",
       "                         50%      75%       max  \n",
       "Avg_Credit_Limit     18000.0  48000.0  200000.0  \n",
       "Total_Credit_Cards       5.0      6.0      10.0  \n",
       "Total_visits_bank        2.0      4.0       5.0  \n",
       "Total_visits_online      2.0      4.0      15.0  \n",
       "Total_calls_made         3.0      5.0      10.0  \n",
       "K_means_segments         0.0      1.0       2.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "markdown",
   "id": "9c425afa",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "Cluster 0:\n",
    "* Credit limit mean is between the the 50% and 75% quartile ($18000 to $48000). \n",
    "* Total cards owned are between 4 and 6 (between 25% and 75% quartile)\n",
    "* Visits to Bank are between 3 and 5 (top 50%)\n",
    "* Visits to Online are between 0 and 2 (bottom 50%)\n",
    "* Total Calls made are between 1 and 3 (bottom 50)\n",
    "\n",
    "Cluster 1:\n",
    "\n",
    "* Credit limit mean is less than $25000\n",
    "* Total cards owned are between 1 and 3 (below 25% quartile) \n",
    "* Visits to Bank are between 0 and 2 (bottom 50%)\n",
    "* Visits to Online are between 2 and 4 (between the the 50% and 75% quartile)\n",
    "* Total Calls made are between 5 and 9 (upper 50%)\n",
    "\n",
    "Cluster 2:\n",
    "\n",
    "* Credit limit mean is at the 75% quartile ($48000). \n",
    "* Total cards owned are between 8 and 10 (above 75% quartile)\n",
    "* Visits to Bank are between 0 and 1 (bottom 25%)\n",
    "* Visits to Online are between 8 and 13 (top 75%)\n",
    "* Total Calls made are between 0 and 2 (bottom 50%) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7c705d",
   "metadata": {},
   "source": [
    "### Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dfb91073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cophenetic correlation for Euclidean distance and single linkage is 0.7391220243806552.\n",
      "Cophenetic correlation for Euclidean distance and complete linkage is 0.8599730607972423.\n",
      "Cophenetic correlation for Euclidean distance and average linkage is 0.8977080867389372.\n",
      "Cophenetic correlation for Euclidean distance and weighted linkage is 0.8861746814895477.\n",
      "Cophenetic correlation for Chebyshev distance and single linkage is 0.7382354769296767.\n",
      "Cophenetic correlation for Chebyshev distance and complete linkage is 0.8533474836336782.\n",
      "Cophenetic correlation for Chebyshev distance and average linkage is 0.8974159511838106.\n",
      "Cophenetic correlation for Chebyshev distance and weighted linkage is 0.8913624010768603.\n",
      "Cophenetic correlation for Mahalanobis distance and single linkage is 0.7058064784553608.\n",
      "Cophenetic correlation for Mahalanobis distance and complete linkage is 0.6663534463875372.\n",
      "Cophenetic correlation for Mahalanobis distance and average linkage is 0.8326994115042138.\n",
      "Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.7805990615142515.\n",
      "Cophenetic correlation for Cityblock distance and single linkage is 0.7252379350252723.\n",
      "Cophenetic correlation for Cityblock distance and complete linkage is 0.8731477899179829.\n",
      "Cophenetic correlation for Cityblock distance and average linkage is 0.896329431104133.\n",
      "Cophenetic correlation for Cityblock distance and weighted linkage is 0.8825520731498188.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"# list of distance metrics\\ndistance_metrics = [\\\"euclidean\\\", \\\"chebyshev\\\", \\\"mahalanobis\\\", \\\"cityblock\\\"]\\n\\n# list of linkage methods\\nlinkage_methods = [\\\"single\\\", \\\"complete\\\", \\\"average\\\", \\\"weighted\\\"]\\n\\nhigh_cophenet_corr = 0\\nhigh_dm_lm = [0, 0]\\n\\nfor dm in distance_metrics:\\n    for lm in linkage_methods:\\n        Z = linkage(DataScaled, metric=dm, method=lm)\\n        c, coph_dists = cophenet(Z, pdist(DataScaled))\\n        print(\\n            \\\"Cophenetic correlation for {} distance and {} linkage is {}.\\\".format(\\n                dm.capitalize(), lm, c\\n            )\\n        )\\n        if high_cophenet_corr < c:\\n            high_cophenet_corr = c\\n            high_dm_lm[0] = dm\\n            high_dm_lm[1] = lm\";\n",
       "                var nbb_formatted_code = \"# list of distance metrics\\ndistance_metrics = [\\\"euclidean\\\", \\\"chebyshev\\\", \\\"mahalanobis\\\", \\\"cityblock\\\"]\\n\\n# list of linkage methods\\nlinkage_methods = [\\\"single\\\", \\\"complete\\\", \\\"average\\\", \\\"weighted\\\"]\\n\\nhigh_cophenet_corr = 0\\nhigh_dm_lm = [0, 0]\\n\\nfor dm in distance_metrics:\\n    for lm in linkage_methods:\\n        Z = linkage(DataScaled, metric=dm, method=lm)\\n        c, coph_dists = cophenet(Z, pdist(DataScaled))\\n        print(\\n            \\\"Cophenetic correlation for {} distance and {} linkage is {}.\\\".format(\\n                dm.capitalize(), lm, c\\n            )\\n        )\\n        if high_cophenet_corr < c:\\n            high_cophenet_corr = c\\n            high_dm_lm[0] = dm\\n            high_dm_lm[1] = lm\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of distance metrics\n",
    "distance_metrics = [\"euclidean\", \"chebyshev\", \"mahalanobis\", \"cityblock\"]\n",
    "\n",
    "# list of linkage methods\n",
    "linkage_methods = [\"single\", \"complete\", \"average\", \"weighted\"]\n",
    "\n",
    "high_cophenet_corr = 0\n",
    "high_dm_lm = [0, 0]\n",
    "\n",
    "for dm in distance_metrics:\n",
    "    for lm in linkage_methods:\n",
    "        Z = linkage(DataScaled, metric=dm, method=lm)\n",
    "        c, coph_dists = cophenet(Z, pdist(DataScaled))\n",
    "        print(\n",
    "            \"Cophenetic correlation for {} distance and {} linkage is {}.\".format(\n",
    "                dm.capitalize(), lm, c\n",
    "            )\n",
    "        )\n",
    "        if high_cophenet_corr < c:\n",
    "            high_cophenet_corr = c\n",
    "            high_dm_lm[0] = dm\n",
    "            high_dm_lm[1] = lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ea4b923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest cophenetic correlation is 0.8977080867389372, which is obtained with Euclidean distance and average linkage.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 36;\n",
       "                var nbb_unformatted_code = \"# printing the combination of distance metric and linkage method with the highest cophenetic correlation\\nprint(\\n    \\\"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.\\\".format(\\n        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]\\n    )\\n)\";\n",
       "                var nbb_formatted_code = \"# printing the combination of distance metric and linkage method with the highest cophenetic correlation\\nprint(\\n    \\\"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.\\\".format(\\n        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]\\n    )\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# printing the combination of distance metric and linkage method with the highest cophenetic correlation\n",
    "print(\n",
    "    \"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.\".format(\n",
    "        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66dfe76e",
   "metadata": {},
   "source": [
    "##### Only Euclidean Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6cd1a6cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cophenetic correlation for single linkage is 0.7391220243806552.\n",
      "Cophenetic correlation for complete linkage is 0.8599730607972423.\n",
      "Cophenetic correlation for average linkage is 0.8977080867389372.\n",
      "Cophenetic correlation for centroid linkage is 0.8939385846326323.\n",
      "Cophenetic correlation for ward linkage is 0.7415156284827493.\n",
      "Cophenetic correlation for weighted linkage is 0.8861746814895477.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 37;\n",
       "                var nbb_unformatted_code = \"# list of linkage methods\\nlinkage_methods = [\\\"single\\\", \\\"complete\\\", \\\"average\\\", \\\"centroid\\\", \\\"ward\\\", \\\"weighted\\\"]\\n\\nhigh_cophenet_corr = 0\\nhigh_dm_lm = [0, 0]\\n\\nfor lm in linkage_methods:\\n    Z = linkage(DataScaled, metric=\\\"euclidean\\\", method=lm)\\n    c, coph_dists = cophenet(Z, pdist(DataScaled))\\n    print(\\\"Cophenetic correlation for {} linkage is {}.\\\".format(lm, c))\\n    if high_cophenet_corr < c:\\n        high_cophenet_corr = c\\n        high_dm_lm[0] = \\\"euclidean\\\"\\n        high_dm_lm[1] = lm\";\n",
       "                var nbb_formatted_code = \"# list of linkage methods\\nlinkage_methods = [\\\"single\\\", \\\"complete\\\", \\\"average\\\", \\\"centroid\\\", \\\"ward\\\", \\\"weighted\\\"]\\n\\nhigh_cophenet_corr = 0\\nhigh_dm_lm = [0, 0]\\n\\nfor lm in linkage_methods:\\n    Z = linkage(DataScaled, metric=\\\"euclidean\\\", method=lm)\\n    c, coph_dists = cophenet(Z, pdist(DataScaled))\\n    print(\\\"Cophenetic correlation for {} linkage is {}.\\\".format(lm, c))\\n    if high_cophenet_corr < c:\\n        high_cophenet_corr = c\\n        high_dm_lm[0] = \\\"euclidean\\\"\\n        high_dm_lm[1] = lm\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of linkage methods\n",
    "linkage_methods = [\"single\", \"complete\", \"average\", \"centroid\", \"ward\", \"weighted\"]\n",
    "\n",
    "high_cophenet_corr = 0\n",
    "high_dm_lm = [0, 0]\n",
    "\n",
    "for lm in linkage_methods:\n",
    "    Z = linkage(DataScaled, metric=\"euclidean\", method=lm)\n",
    "    c, coph_dists = cophenet(Z, pdist(DataScaled))\n",
    "    print(\"Cophenetic correlation for {} linkage is {}.\".format(lm, c))\n",
    "    if high_cophenet_corr < c:\n",
    "        high_cophenet_corr = c\n",
    "        high_dm_lm[0] = \"euclidean\"\n",
    "        high_dm_lm[1] = lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "747c28c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest cophenetic correlation is 0.8977080867389372, which is obtained with Euclidean distance and average linkage.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 38;\n",
       "                var nbb_unformatted_code = \"# printing the combination of distance metric and linkage method with the highest cophenetic correlation\\nprint(\\n    \\\"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.\\\".format(\\n        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]\\n    )\\n)\";\n",
       "                var nbb_formatted_code = \"# printing the combination of distance metric and linkage method with the highest cophenetic correlation\\nprint(\\n    \\\"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.\\\".format(\\n        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]\\n    )\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# printing the combination of distance metric and linkage method with the highest cophenetic correlation\n",
    "print(\n",
    "    \"Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.\".format(\n",
    "        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96fbb6e",
   "metadata": {},
   "source": [
    "* The cophenetic correlation is maximum with Euclidean distance and average linkage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c9e7d9",
   "metadata": {},
   "source": [
    "#### Dendrograms for the different linkage methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8909e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2160 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 39;\n",
       "                var nbb_unformatted_code = \"# list of linkage methods\\nlinkage_methods = [\\\"single\\\", \\\"complete\\\", \\\"average\\\", \\\"centroid\\\", \\\"ward\\\", \\\"weighted\\\"]\\n\\n# lists to save results of cophenetic correlation calculation\\ncompare_cols = [\\\"Linkage\\\", \\\"Cophenetic Coefficient\\\"]\\n\\n# to create a subplot image\\nfig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))\\n\\n# We will enumerate through the list of linkage methods above\\n# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation\\nfor i, method in enumerate(linkage_methods):\\n    Z = linkage(DataScaled, metric=\\\"euclidean\\\", method=method)\\n\\n    dendrogram(Z, ax=axs[i])\\n    axs[i].set_title(f\\\"Dendrogram ({method.capitalize()} Linkage)\\\")\\n\\n    coph_corr, coph_dist = cophenet(Z, pdist(DataScaled))\\n    axs[i].annotate(\\n        f\\\"Cophenetic\\\\nCorrelation\\\\n{coph_corr:0.2f}\\\",\\n        (0.80, 0.80),\\n        xycoords=\\\"axes fraction\\\",\\n    )\";\n",
       "                var nbb_formatted_code = \"# list of linkage methods\\nlinkage_methods = [\\\"single\\\", \\\"complete\\\", \\\"average\\\", \\\"centroid\\\", \\\"ward\\\", \\\"weighted\\\"]\\n\\n# lists to save results of cophenetic correlation calculation\\ncompare_cols = [\\\"Linkage\\\", \\\"Cophenetic Coefficient\\\"]\\n\\n# to create a subplot image\\nfig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))\\n\\n# We will enumerate through the list of linkage methods above\\n# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation\\nfor i, method in enumerate(linkage_methods):\\n    Z = linkage(DataScaled, metric=\\\"euclidean\\\", method=method)\\n\\n    dendrogram(Z, ax=axs[i])\\n    axs[i].set_title(f\\\"Dendrogram ({method.capitalize()} Linkage)\\\")\\n\\n    coph_corr, coph_dist = cophenet(Z, pdist(DataScaled))\\n    axs[i].annotate(\\n        f\\\"Cophenetic\\\\nCorrelation\\\\n{coph_corr:0.2f}\\\",\\n        (0.80, 0.80),\\n        xycoords=\\\"axes fraction\\\",\\n    )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of linkage methods\n",
    "linkage_methods = [\"single\", \"complete\", \"average\", \"centroid\", \"ward\", \"weighted\"]\n",
    "\n",
    "# lists to save results of cophenetic correlation calculation\n",
    "compare_cols = [\"Linkage\", \"Cophenetic Coefficient\"]\n",
    "\n",
    "# to create a subplot image\n",
    "fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))\n",
    "\n",
    "# We will enumerate through the list of linkage methods above\n",
    "# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation\n",
    "for i, method in enumerate(linkage_methods):\n",
    "    Z = linkage(DataScaled, metric=\"euclidean\", method=method)\n",
    "\n",
    "    dendrogram(Z, ax=axs[i])\n",
    "    axs[i].set_title(f\"Dendrogram ({method.capitalize()} Linkage)\")\n",
    "\n",
    "    coph_corr, coph_dist = cophenet(Z, pdist(DataScaled))\n",
    "    axs[i].annotate(\n",
    "        f\"Cophenetic\\nCorrelation\\n{coph_corr:0.2f}\",\n",
    "        (0.80, 0.80),\n",
    "        xycoords=\"axes fraction\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb17c89",
   "metadata": {},
   "source": [
    "* Average Linkage has the highest cophenetic correlation among the linkage methods. \n",
    "* 3 appears to be the appropriate number of clusters from the dendrogram for average linkage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dd8a7a1",
   "metadata": {},
   "source": [
    "#### Cluster Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2dca1d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(linkage='average', n_clusters=3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 40;\n",
       "                var nbb_unformatted_code = \"HCmodel = AgglomerativeClustering(n_clusters=3, affinity=\\\"euclidean\\\", linkage=\\\"average\\\")\\nHCmodel.fit(DataScaled)\";\n",
       "                var nbb_formatted_code = \"HCmodel = AgglomerativeClustering(n_clusters=3, affinity=\\\"euclidean\\\", linkage=\\\"average\\\")\\nHCmodel.fit(DataScaled)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HCmodel = AgglomerativeClustering(n_clusters=3, affinity=\"euclidean\", linkage=\"average\")\n",
    "HCmodel.fit(DataScaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cbd3af7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 41;\n",
       "                var nbb_unformatted_code = \"DataScaled[\\\"HC_Clusters\\\"] = HCmodel.labels_\\ndata[\\\"HC_Clusters\\\"] = HCmodel.labels_\";\n",
       "                var nbb_formatted_code = \"DataScaled[\\\"HC_Clusters\\\"] = HCmodel.labels_\\ndata[\\\"HC_Clusters\\\"] = HCmodel.labels_\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DataScaled[\"HC_Clusters\"] = HCmodel.labels_\n",
    "data[\"HC_Clusters\"] = HCmodel.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0492effe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 42;\n",
       "                var nbb_unformatted_code = \"cluster_profile_HC = data.groupby(\\\"HC_Clusters\\\").mean()\";\n",
       "                var nbb_formatted_code = \"cluster_profile_HC = data.groupby(\\\"HC_Clusters\\\").mean()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_profile_HC = data.groupby(\"HC_Clusters\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f890c87f",
   "metadata": {},
   "source": [
    "#### Favorate is K-means:\n",
    "* K-means was more clear in choosing the number of clusters\n",
    "* Vizualizations available to K-means are easier to interpret and use.\n",
    "\n",
    "#### Pros of Heirachial Clustering: \n",
    "* Heirachial clustering was good for picking a linkage type\n",
    "* Shows cophenetic correlation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97bcd25f",
   "metadata": {},
   "source": [
    "#### Heirachial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b439028f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_4ad1e_row0_col2,#T_4ad1e_row1_col0,#T_4ad1e_row1_col1,#T_4ad1e_row1_col3,#T_4ad1e_row1_col5,#T_4ad1e_row2_col4{\n",
       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_4ad1e_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Avg_Credit_Limit</th>        <th class=\"col_heading level0 col1\" >Total_Credit_Cards</th>        <th class=\"col_heading level0 col2\" >Total_visits_bank</th>        <th class=\"col_heading level0 col3\" >Total_visits_online</th>        <th class=\"col_heading level0 col4\" >Total_calls_made</th>        <th class=\"col_heading level0 col5\" >K_means_segments</th>    </tr>    <tr>        <th class=\"index_name level0\" >HC_Clusters</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4ad1e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_4ad1e_row0_col0\" class=\"data row0 col0\" >33713.178295</td>\n",
       "                        <td id=\"T_4ad1e_row0_col1\" class=\"data row0 col1\" >5.511628</td>\n",
       "                        <td id=\"T_4ad1e_row0_col2\" class=\"data row0 col2\" >3.485788</td>\n",
       "                        <td id=\"T_4ad1e_row0_col3\" class=\"data row0 col3\" >0.984496</td>\n",
       "                        <td id=\"T_4ad1e_row0_col4\" class=\"data row0 col4\" >2.005168</td>\n",
       "                        <td id=\"T_4ad1e_row0_col5\" class=\"data row0 col5\" >0.002584</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ad1e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_4ad1e_row1_col0\" class=\"data row1 col0\" >141040.000000</td>\n",
       "                        <td id=\"T_4ad1e_row1_col1\" class=\"data row1 col1\" >8.740000</td>\n",
       "                        <td id=\"T_4ad1e_row1_col2\" class=\"data row1 col2\" >0.600000</td>\n",
       "                        <td id=\"T_4ad1e_row1_col3\" class=\"data row1 col3\" >10.900000</td>\n",
       "                        <td id=\"T_4ad1e_row1_col4\" class=\"data row1 col4\" >1.080000</td>\n",
       "                        <td id=\"T_4ad1e_row1_col5\" class=\"data row1 col5\" >2.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4ad1e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_4ad1e_row2_col0\" class=\"data row2 col0\" >12197.309417</td>\n",
       "                        <td id=\"T_4ad1e_row2_col1\" class=\"data row2 col1\" >2.403587</td>\n",
       "                        <td id=\"T_4ad1e_row2_col2\" class=\"data row2 col2\" >0.928251</td>\n",
       "                        <td id=\"T_4ad1e_row2_col3\" class=\"data row2 col3\" >3.560538</td>\n",
       "                        <td id=\"T_4ad1e_row2_col4\" class=\"data row2 col4\" >6.883408</td>\n",
       "                        <td id=\"T_4ad1e_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x216cf6cdb50>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 43;\n",
       "                var nbb_unformatted_code = \"# lets display cluster profile\\ncluster_profile_HC.style.highlight_max(color=\\\"lightgreen\\\", axis=0)\";\n",
       "                var nbb_formatted_code = \"# lets display cluster profile\\ncluster_profile_HC.style.highlight_max(color=\\\"lightgreen\\\", axis=0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets display cluster profile \n",
    "cluster_profile_HC.style.highlight_max(color=\"lightgreen\", axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73d03a1",
   "metadata": {},
   "source": [
    "#### K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0d5a297a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_899dd_row0_col2,#T_899dd_row0_col5,#T_899dd_row1_col4,#T_899dd_row2_col0,#T_899dd_row2_col1,#T_899dd_row2_col3{\n",
       "            background-color:  green;\n",
       "        }</style><table id=\"T_899dd_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Avg_Credit_Limit</th>        <th class=\"col_heading level0 col1\" >Total_Credit_Cards</th>        <th class=\"col_heading level0 col2\" >Total_visits_bank</th>        <th class=\"col_heading level0 col3\" >Total_visits_online</th>        <th class=\"col_heading level0 col4\" >Total_calls_made</th>        <th class=\"col_heading level0 col5\" >count_in_each_segment</th>    </tr>    <tr>        <th class=\"index_name level0\" >K_means_segments</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_899dd_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_899dd_row0_col0\" class=\"data row0 col0\" >33782.383420</td>\n",
       "                        <td id=\"T_899dd_row0_col1\" class=\"data row0 col1\" >5.515544</td>\n",
       "                        <td id=\"T_899dd_row0_col2\" class=\"data row0 col2\" >3.489637</td>\n",
       "                        <td id=\"T_899dd_row0_col3\" class=\"data row0 col3\" >0.981865</td>\n",
       "                        <td id=\"T_899dd_row0_col4\" class=\"data row0 col4\" >2.000000</td>\n",
       "                        <td id=\"T_899dd_row0_col5\" class=\"data row0 col5\" >386</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_899dd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_899dd_row1_col0\" class=\"data row1 col0\" >12174.107143</td>\n",
       "                        <td id=\"T_899dd_row1_col1\" class=\"data row1 col1\" >2.410714</td>\n",
       "                        <td id=\"T_899dd_row1_col2\" class=\"data row1 col2\" >0.933036</td>\n",
       "                        <td id=\"T_899dd_row1_col3\" class=\"data row1 col3\" >3.553571</td>\n",
       "                        <td id=\"T_899dd_row1_col4\" class=\"data row1 col4\" >6.870536</td>\n",
       "                        <td id=\"T_899dd_row1_col5\" class=\"data row1 col5\" >224</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_899dd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_899dd_row2_col0\" class=\"data row2 col0\" >141040.000000</td>\n",
       "                        <td id=\"T_899dd_row2_col1\" class=\"data row2 col1\" >8.740000</td>\n",
       "                        <td id=\"T_899dd_row2_col2\" class=\"data row2 col2\" >0.600000</td>\n",
       "                        <td id=\"T_899dd_row2_col3\" class=\"data row2 col3\" >10.900000</td>\n",
       "                        <td id=\"T_899dd_row2_col4\" class=\"data row2 col4\" >1.080000</td>\n",
       "                        <td id=\"T_899dd_row2_col5\" class=\"data row2 col5\" >50</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x216cf686b20>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 45;\n",
       "                var nbb_unformatted_code = \"# lets display cluster profile \\ncluster_profile.style.highlight_max(color=\\\"green\\\", axis=0)\";\n",
       "                var nbb_formatted_code = \"# lets display cluster profile\\ncluster_profile.style.highlight_max(color=\\\"green\\\", axis=0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets display cluster profile\n",
    "cluster_profile.style.highlight_max(color=\"green\", axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f4fea7",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "* Cluster 0 from both clustering methods is grouped similar.\n",
    "* Clusters 1 and 2 looked switched between the two methods. \n",
    "* It appears both methods came to the same conclusion when assighed to 3 clusters. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a59399e6",
   "metadata": {},
   "source": [
    "### Action Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f84f16",
   "metadata": {},
   "source": [
    "The three clusters is judged by the k-means method. Our three groups of customers is shown below.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4ba86a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
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    "fig, axes = plt.subplots(1, 5, figsize=(16, 6))\n",
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  {
   "cell_type": "markdown",
   "id": "1330d689",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "Cluster 0:\n",
    "\n",
    "* Credit limit mean is between the 50% and 75% quartile (18000 to 48000). \n",
    "* Total cards owned are between 4 and 6 (between 25% and 75% quartile)\n",
    "* Visits to Bank are between 3 and 5 (top 50%)\n",
    "* Visits to Online are between 0 and 2 (bottom 50%)\n",
    "* Total Calls made are between 1 and 3 (bottom 50)\n",
    "\n",
    "Cluster 1:\n",
    "\n",
    "* Credit limit mean is less than $25000\n",
    "* Total cards owned are between 1 and 3 (below 25% quartile) \n",
    "* Visits to Bank are between 0 and 2 (bottom 50%)\n",
    "* Visits to Online are between 2 and 4 (between the the 50% and 75% quartile)\n",
    "* Total Calls made are between 5 and 9 (upper 50%)\n",
    "\n",
    "Cluster 2:\n",
    "\n",
    "* Credit limit mean is at the 75% quartile ($48000). \n",
    "* Total cards owned are between 8 and 10 (above 75% quartile)\n",
    "* Visits to Bank are between 0 and 1 (bottom 25%)\n",
    "* Visits to Online are between 8 and 13 (top 75%)\n",
    "* Total Calls made are between 0 and 2 (bottom 50%) \n",
    "\n",
    "\n",
    "#### Market Conclusion: \n",
    "Cluster 2 is the most satisfied because they have the highest credit card limit and repeatedly purchase the most credit cards.No marketing should be done on phone calls because they are the least satisfied. Most sales should be done at the bank. Online banking seems to build higher credit card limits without the need for more cards. Therefore, they lower dependency on credit card ownership. Since cluster 1 owns the least cards they need to be targeted for upselling so they can join the model group, cluster 2, which makes the bank the most revenu. Finding ways to encourage in person banking will increase sales.\n",
    "\n",
    "#### Service Conclusion:\n",
    "The customer service team should focus on improving the call service because the most unsatisfied customers are calling. Online services should guide the customers through the online portal with them to help introduce/encourage online banking because the most successful customers are banking online.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b891649",
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