CUSTOMER SEGMENTATION FOR FINANCIAL SERVICES FIRM USING K-MEANS CLUSTERING ALGORITHM .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import the relevant libraries or packages and modify the settings\n",
    "\n",
    "My first step will be to import all the relevant libraries and packages that will allow you to perform cluster analysis and to visualise the results.\n",
    "\n",
    "The most commonly used packages can be imported with the following lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from __future__ import division\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime as dt\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WIth the following code cell, you can set the colours and palettes for the seaborn graphing package that you imported in the previous step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = '#e63635'\n",
    "pgrey = '#3c3f41'\n",
    "mgrey = '#b2b3b3'\n",
    "lgrey = '#edebeb'\n",
    "purp = '#ab66cc'\n",
    "amet = '#8371e2'\n",
    "turq = '#7ce6d8'\n",
    "blue = '#55acee'\n",
    "whit = '#ffffff'\n",
    "\n",
    "# Define a colour palette\n",
    "LSEpalt1 = [pred, pgrey, purp, amet]\n",
    "LSEpalt2 = [pred, purp, turq, blue]\n",
    "LSEpalt3 = [pred, purp, turq, amet, blue]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT:** To ensure that the results are consistent (no matter how many times you perform or run the analysis), it's important to set a random number seed, which ensures better replicability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Importing, reading, and checking the data file\n",
    "\n",
    "With the relevant libraries and packages successfully imported, the next step is to import the data file and ensure that the data loads correctly. In this case, you will import a csv data  which is a collection of ABC customer demographic and transactional data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.read_csv(r\"C:\\Users\\USER\\OneDrive\\Desktop\\EVERYTHING DATA\\DATASETS\\ABC.csv\", error_bad_lines = False, engine='python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have imported the data, it's important to check that the data that has been loaded is correct. This can be assessed by reading in the row and column names of the data set and ensuring that these align with the correct data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Investor_id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>ComfortDigital</th>\n",
       "      <th>KnowledgeFM</th>\n",
       "      <th>FrequencyTransact</th>\n",
       "      <th>RiskAversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "<p>8000 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "      Investor_id  Gender  Age  ComfortDigital  KnowledgeFM  \\\n",
       "0          100001       1   23               4            1   \n",
       "1          100002       1   66               2            5   \n",
       "2          100003       0   25               5            1   \n",
       "3          100004       1   18               5            1   \n",
       "4          100005       1   70               1            3   \n",
       "...           ...     ...  ...             ...          ...   \n",
       "7995       107996       0   49               1            5   \n",
       "7996       107997       1   24               5            1   \n",
       "7997       107998       1   46               3            4   \n",
       "7998       107999       0   24               5            1   \n",
       "7999       108000       1   25               5            2   \n",
       "\n",
       "      FrequencyTransact  RiskAversion  \n",
       "0                     5             1  \n",
       "1                     2             5  \n",
       "2                     2             1  \n",
       "3                     4             1  \n",
       "4                     4             5  \n",
       "...                 ...           ...  \n",
       "7995                  1             4  \n",
       "7996                  1             1  \n",
       "7997                  2             5  \n",
       "7998                  3             1  \n",
       "7999                  4             1  \n",
       "\n",
       "[8000 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, you can analyse what the data set looks like\n",
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " rows read in =  (8000, 7)\n",
      " rows after dropna =  (8000, 7)\n"
     ]
    }
   ],
   "source": [
    "print(\" rows read in = \", df0.shape)\n",
    "df0 = df0.dropna()\n",
    "print(\" rows after dropna = \", df0.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the correct data set is loaded, I can use this data to help segment customers into distinct clusters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Cluster analysis for customer segmentation\n",
    "\n",
    "The business, ABC, is trying to decide which products to provide to which customers. In other words, which customers ought to be provided which products? Conducting a segmentation analysis is the first step in finding the answer to this query. A formal method for creating client segments is cluster analysis which I will be creating meaningful segments for ABC using k-means cluster algorithm. \n",
    "\n",
    "I, the marketing analyst, is required to specify the ideal number of clusters in the first step of the procedure. I can choose an ideal quantity using a variety of statistical factors in addition to managerial discretion. The approach that plots the inside sum-of-squares (WSS) values for various cluster densities. These lines of code are used to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The code below produces the elbow function and chart.\n",
    "\n",
    "df_cluster = df0[['Gender', 'Age', 'ComfortDigital', 'KnowledgeFM','FrequencyTransact', 'RiskAversion']]\n",
    "df_cluster_array = df_cluster.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This user-defined function can be used to create the elbow curve.\n",
    "\n",
    "def udf_cluster_elbow(p_arr, p_maxn, p_title, p_xlabel, p_save):\n",
    "    sse={}\n",
    "    for k in range(1,p_maxn):\n",
    "        kmeans = KMeans(n_clusters=k, max_iter=1000).fit(p_arr)\n",
    "        sse[k] = kmeans.inertia_ \n",
    "    \n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.plot(list(sse.keys()), list(sse.values()), color=\"#e63635\", linewidth=3.0)\n",
    "    plt.title(p_title, size=24)\n",
    "    plt.xlabel(p_xlabel, size=18, fontweight='bold')\n",
    "    plt.ylabel('Within sum-of-squares (WSS)', size=18, fontweight='bold')\n",
    "    if  (len(p_save) > 0):\n",
    "        plt.savefig(p_save)\n",
    "    else:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the elbow.\n",
    "\n",
    "udf_cluster_elbow(p_arr=df_cluster_array, p_maxn=7, p_title=\"Elbow chart\", p_xlabel=\"Number of clusters\", p_save='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on what I know about the elbow method, the number of clusters would you select as most optimal for this data set is two clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Fitting the data to the optimal number of clusters\n",
    "We now have a rough sense of the ideal number clusters based on the elbow chart I made earlier. We will proceed with this task by identifying two clusters as the best possible outcome. \n",
    "\n",
    "\n",
    "We can carry out the k-means clustering technique using the following lines of code and determine whether the resulting clusters are significant and practical for business needs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## The first column of the data set is an investor ID (This needs to be kept so that it can be tied back to the data).\n",
    "\n",
    "df1 = df0[['Investor_id', 'Gender', 'Age', 'ComfortDigital', 'KnowledgeFM','FrequencyTransact', 'RiskAversion']]\n",
    "def udf_kmeans(p_df, p_n, p_scale=0):\n",
    "    f1_df = p_df.iloc[:,1:p_df.shape[1]] \n",
    "    if   (p_scale==0):\n",
    "         f1_df_s = f1_df\n",
    "    else:\n",
    "         f1_df_s = StandardScaler().fit_transform(p1_df)\n",
    "         \n",
    "    f1_df_array = f1_df_s.to_numpy()\n",
    "    \n",
    "    kmeans = KMeans(n_clusters=p_n, random_state=2493)\n",
    "    kmeans.fit(f1_df_array)\n",
    "    p_df['ClusterNum'] = kmeans.predict(f1_df_array)\n",
    "    o_stat = p_df.iloc[:,1:p_df.shape[1]].groupby('ClusterNum').describe()\n",
    "    stat2 = o_stat.T\n",
    "    o_df_means = pd.DataFrame()\n",
    "    o_df_medians = pd.DataFrame()\n",
    "    o_df_cov = pd.DataFrame()\n",
    "    for i in range(1, p_df.shape[1]-1):\n",
    "        colname=p_df.columns.values[i]\n",
    "        o_df_means[colname] = o_stat[colname]['mean'] \n",
    "        o_df_medians[colname] = o_stat[colname]['50%'] \n",
    "        o_df_cov[colname] = o_stat[colname]['std']/o_stat[colname]['mean']\n",
    "    \n",
    "    o_df_means.index = range(0,p_n)\n",
    "    o_df_medians.index = range(0,p_n)\n",
    "\n",
    "    return p_df, o_df_means, o_df_medians, o_df_cov\n",
    "    \n",
    "\n",
    "df1_c, df1_means, df1_medians, df1_cov = udf_kmeans(p_df=df1, p_n=2, p_scale=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps for the <i>k</i>-means clustering algorithm are now complete.\n",
    "\n",
    "Next, you can move on to name the clusters. To achieve this meaningfully, you first need to examine how the averages (or other descriptive statistics) of the variables differ across different clusters that were created. The following lines of code first identify how many observations (or data points) are housed within each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "counts of observations in each cluster\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    5985\n",
       "0    2015\n",
       "Name: ClusterNum, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the counts of observations (i.e. number of people) in each cluster.\n",
    "\n",
    "print('counts of observations in each cluster')\n",
    "df1_c.ClusterNum.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You notice that the clusters are fairly large in size, with a large number of people housed within each.\n",
    "\n",
    "Next, I will calculate the mean value for each variable within the two clusters that were created using the following lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2)\n",
    "kmeans.fit(df_cluster_array)\n",
    "df1['ClusterNum'] = kmeans.predict(df_cluster_array)\n",
    "stat1 = df1.groupby('ClusterNum').describe()\n",
    "stat2 = stat1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>ComfortDigital</th>\n",
       "      <th>KnowledgeFM</th>\n",
       "      <th>FrequencyTransact</th>\n",
       "      <th>RiskAversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.551044</td>\n",
       "      <td>25.224227</td>\n",
       "      <td>3.980618</td>\n",
       "      <td>2.063158</td>\n",
       "      <td>4.138179</td>\n",
       "      <td>1.668003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.556328</td>\n",
       "      <td>56.017370</td>\n",
       "      <td>2.407940</td>\n",
       "      <td>3.585608</td>\n",
       "      <td>2.368734</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender        Age  ComfortDigital  KnowledgeFM  FrequencyTransact  \\\n",
       "0  0.551044  25.224227        3.980618     2.063158           4.138179   \n",
       "1  0.556328  56.017370        2.407940     3.585608           2.368734   \n",
       "\n",
       "   RiskAversion  \n",
       "0      1.668003  \n",
       "1      3.846154  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, you can view what the means of the variables look like across the 2 different clusters.\n",
    "\n",
    "df_means = pd.DataFrame({\n",
    "            'Gender': stat1['Gender']['mean'] \n",
    "            , 'Age': stat1['Age']['mean'] \n",
    "            , 'ComfortDigital': stat1['ComfortDigital']['mean'] \n",
    "            , 'KnowledgeFM': stat1['KnowledgeFM']['mean'] \n",
    "            , 'FrequencyTransact': stat1['FrequencyTransact']['mean'] \n",
    "            , 'RiskAversion': stat1['RiskAversion']['mean']           \n",
    "            }, index=[0,1])\n",
    "df_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the output, can you create meaningful descriptions of the two clusters? In other words, can you differentiate them in terms of the six variables that describe the customers?\n",
    "\n",
    "Customers in Cluster 0 are the youngest with an average age of 25 years old, highly comfortable with using digital platforms to conduct financial transactions, have low knowledge about the financial markets, low on risk aversion, and are very high on transaction frequency, in other words, they are young individuals who prefer digital methods of conducting financial transactions basically tech-savvy. They have low knowledge about financial markets, indicating they are new investors who are new to investing. They are low on risk aversion and have a very high frequency of transactions indicating they are active traders who prefer growth-oriented investment products such as stocks that offer higher returns but are considered riskier.\n",
    "\n",
    "Customers in Cluster 1 are the oldest with an average age of 56 years old, they have the lowest level of comfort with using digital platforms to conduct financial transactions, have high knowledge about the financial markets, are very high on risk aversion, and are very low on transaction frequency, in other words, they are middle-aged individuals who prefer traditional methods of conducting financial transactions and have low comfort levels with using digital platforms. They have a high level of knowledge about financial markets, indicating that they may be experienced investors. They are very risk-averse, meaning that they are more likely to choose investment products that offer lower returns but are considered safer. Finally, they have a very low frequency of transactions, indicating that they are not active traders\n",
    "\n",
    "Although, there is a similarity observed. I noticed that both clusters  seem evenly divided as far as gender is concerned meaning that both clusters are very similar with respect to gender variable.\n",
    "\n",
    "\n",
    "Next, you will look at what the medians of the variables look like across the two different clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>ComfortDigital</th>\n",
       "      <th>KnowledgeFM</th>\n",
       "      <th>FrequencyTransact</th>\n",
       "      <th>RiskAversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  ComfortDigital  KnowledgeFM  FrequencyTransact  RiskAversion\n",
       "0     1.0  24.0             4.0          2.0                5.0           1.0\n",
       "1     1.0  57.0             2.0          4.0                2.0           5.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_medians = pd.DataFrame({\n",
    "            'Gender': stat1['Gender']['50%'] \n",
    "            , 'Age': stat1['Age']['50%'] \n",
    "            , 'ComfortDigital': stat1['ComfortDigital']['50%'] \n",
    "            , 'KnowledgeFM': stat1['KnowledgeFM']['50%'] \n",
    "            , 'FrequencyTransact': stat1['FrequencyTransact']['50%'] \n",
    "            , 'RiskAversion': stat1['RiskAversion']['50%'] \n",
    "            }, index=[0,1])\n",
    "\n",
    "df_medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>ClusterNum</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Investor_id</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>104014.059983</td>\n",
       "      <td>103960.223821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2319.657323</td>\n",
       "      <td>2279.341316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100001.000000</td>\n",
       "      <td>100002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>102013.000000</td>\n",
       "      <td>101947.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103991.000000</td>\n",
       "      <td>104021.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>106035.000000</td>\n",
       "      <td>105905.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>108000.000000</td>\n",
       "      <td>107998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Gender</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.551044</td>\n",
       "      <td>0.556328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.497429</td>\n",
       "      <td>0.496940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Age</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.224227</td>\n",
       "      <td>56.017370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.526162</td>\n",
       "      <td>8.292397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">ComfortDigital</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.980618</td>\n",
       "      <td>2.407940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.204087</td>\n",
       "      <td>1.412575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">KnowledgeFM</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.063158</td>\n",
       "      <td>3.585608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.290269</td>\n",
       "      <td>1.500576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">FrequencyTransact</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.138179</td>\n",
       "      <td>2.368734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.315575</td>\n",
       "      <td>1.470503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">RiskAversion</th>\n",
       "      <th>count</th>\n",
       "      <td>5985.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.668003</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.213871</td>\n",
       "      <td>1.504760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ClusterNum                           0              1\n",
       "Investor_id       count    5985.000000    2015.000000\n",
       "                  mean   104014.059983  103960.223821\n",
       "                  std      2319.657323    2279.341316\n",
       "                  min    100001.000000  100002.000000\n",
       "                  25%    102013.000000  101947.000000\n",
       "                  50%    103991.000000  104021.000000\n",
       "                  75%    106035.000000  105905.500000\n",
       "                  max    108000.000000  107998.000000\n",
       "Gender            count    5985.000000    2015.000000\n",
       "                  mean        0.551044       0.556328\n",
       "                  std         0.497429       0.496940\n",
       "                  min         0.000000       0.000000\n",
       "                  25%         0.000000       0.000000\n",
       "                  50%         1.000000       1.000000\n",
       "                  75%         1.000000       1.000000\n",
       "                  max         1.000000       1.000000\n",
       "Age               count    5985.000000    2015.000000\n",
       "                  mean       25.224227      56.017370\n",
       "                  std         4.526162       8.292397\n",
       "                  min        18.000000      41.000000\n",
       "                  25%        23.000000      49.000000\n",
       "                  50%        24.000000      57.000000\n",
       "                  75%        26.000000      62.000000\n",
       "                  max        41.000000      79.000000\n",
       "ComfortDigital    count    5985.000000    2015.000000\n",
       "                  mean        3.980618       2.407940\n",
       "                  std         1.204087       1.412575\n",
       "                  min         1.000000       1.000000\n",
       "                  25%         3.000000       1.000000\n",
       "                  50%         4.000000       2.000000\n",
       "                  75%         5.000000       4.000000\n",
       "                  max         5.000000       5.000000\n",
       "KnowledgeFM       count    5985.000000    2015.000000\n",
       "                  mean        2.063158       3.585608\n",
       "                  std         1.290269       1.500576\n",
       "                  min         1.000000       1.000000\n",
       "                  25%         1.000000       2.000000\n",
       "                  50%         2.000000       4.000000\n",
       "                  75%         3.000000       5.000000\n",
       "                  max         5.000000       5.000000\n",
       "FrequencyTransact count    5985.000000    2015.000000\n",
       "                  mean        4.138179       2.368734\n",
       "                  std         1.315575       1.470503\n",
       "                  min         1.000000       1.000000\n",
       "                  25%         3.000000       1.000000\n",
       "                  50%         5.000000       2.000000\n",
       "                  75%         5.000000       3.000000\n",
       "                  max         5.000000       5.000000\n",
       "RiskAversion      count    5985.000000    2015.000000\n",
       "                  mean        1.668003       3.846154\n",
       "                  std         1.213871       1.504760\n",
       "                  min         1.000000       1.000000\n",
       "                  25%         1.000000       3.000000\n",
       "                  50%         1.000000       5.000000\n",
       "                  75%         2.000000       5.000000\n",
       "                  max         5.000000       5.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its important to note that, while requesting the medians, I will also be able to receive information on the variable averages (or means). In fact, you can request information on the mean, median, standard deviation, minimum, maximum and the three quartiles (25%, 50%, and 75%). This would give me a more complete picture of the different clusters and what variables describe and distinguish them from one another. This can be achieved using the `stat2` command. \n",
    "\n",
    "<b>Note:</b> We can ignore statistics for the `Investor_id` variable, since descriptive statistics on this variable are meaningless. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Individual plots of cluster features: a visual story\n",
    "\n",
    "Next, we can create graphical outputs of the variables that make up each of the two clusters using some visualisation techniques.\n",
    "\n",
    "The first I will create are jitter plots (overlayed on top of box plots) for the various variables in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+s8D3wpcPuPtY10AUGZe5V7q3QEREZPYJL1S9j+DyHivcfdM4dZ8BVgLvdvdrxqonMh5tSRMREZmc9xEkaM9OkKC9iiBB6yO4Z6fIIRnv6tEiIiKzipl9hmC3+w89uC8vZrYAeA8jZ6H+6zjTz2XkrNzr3L17rLoiE9HuThERkZCZ3cXIWZmD4aOlqMrXgHd6yY+nmX2a4IbtCwiOgdtLcKxq2WPfRCZDuztFRERG/CPBWdNPEpwRWg/sJjjZ6FJ3f0dpghaaS3DSzADBZXkuVoImh0tb0kREREQi6Ig7Jm3u3Lm+fPnyaochIiIiMqEHH3xwr7u3lys74pK05cuXs379+mqHISIiIjIhM9s8VpmOSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQVVN0sxsk5k9bmaPmNn6MuVmZp81s41m9piZnVmNOEVERERmWqLaAQAXufveMcpeB6wMHy8DvhAORURERI5oUUjSxvMm4AZ3d+BeM2sxs4XuvqPagVXF+g/CM/9a7SgmkAAcyE1BW0lgeAraKZYCMlPc5mTEmZplMlk1wOAUtJMGhibf5+X9YDYF/cqMymVg2/dgcA/0PQ+dG6B+GRz9Ttj8LdjzK5h3Psx9OWz7Aay4AnwQPAv5DMw9HzZ/G2rag+m23QrJZmi/ALZ9F7JD0LYKjvptePpz0HA0pOdAsgX23QeJJth6E7SeBQPboHUVWBxaT4WaBbDhk9B0EsRi0PkkDO6CVBM0rIAVb4fhHth7P5hD22poOwOe/HfY/0jQ5/6HYPk7YGgn5Abghf+G494Pmb0w75VBDI0nQucjQd8v/g8sfC30vQDzL4b8EDz895Drg4W/AalmWPDq0ctw952w/1FIz4WBHdB2Juz8BQzthpZVMLgD4rXQ8QDks5AfhBM+AL3PwZzzYP96SC+AnT+B0/4hiKHtLNh6S7C8BnZCy8mQ7Q+WTaoVckOw/YdQtygYv+duWPluWPa7QUy77oQNn4BFvxXU698clLecAq2nwb6HYdfPgvV8zx9DZjcsewuk2oL5OfGv4KlPQ818wKHpRNj2IxjaA8lWaDkekk2w7LJgPod7g35bToGdP4PBveAZaDkNmlYCBh0PB8sp1QxD+8CSMNwN/S9Cej7MWQX7HoL5F0HXY9B6RvBeWfyGaf8YRJEF+U+VOjd7AdhP8Kv+RXe/tqT8+8A/u/td4eufA3/j7gftGi1YvXq1r18/ZvGUuv7669m0adOM9AXw0ZUf0++fRNrG3mX89/YrZqSv5cuXc8UVM9PXEe/X/wKP/E2ZAiP4ep5g/JxzYd+94YsYkA+fl/xzMvflsPeeAy9zeSNmQTul323ukPUEQ/kUDYl+3IM6hWFBR6aVpkQXiVg+bDPGdVuu5E+WfflAO2bQl62jvqid4XycZCzHA/vP4uzWB9k6sJgltdvYPjifRTW76M3W05Do49vbf4956d2smXP7qL7/c9N72JNpByBlQ/ztsf9MrCi+vEPMGBVDqUKdTC5BKp49UG/H4DwW1uwmlzfiscp+o93hMy/8BT3ZJv7vyo8TNy/b/6Pdp3FK4xPELc9QNkY6kR/Vhhnk80FePKF4A+R6R16n5wVJW7FkE2QHg6TtUFzaESSmRyAze9DdV5crq/aWtPPdfbuZzQN+amZPufsdReXlUpKD3rFmdhVwFcDSpUunJ9IyNm3axHMbNjA/n5+48hQYPhqS8RnpSuSQ/ODepfR2PT7t/eya1C+HTNqi1wdbyPq2wtAuyPVDrAYWXgI7boP8AMTroOFY6H0G2i8Mtm4MbA+mP+ZP4PHdULMQahcFW1HiNTD3XNj1y2BLXdNKOO7PoHcTHf0xdvQ2kvEky2s34240JbvIeIoEWXqyTbgZWweOoiPTwivm3M1gLs2wJ2hM9BB3Jwdk8rU82HUGjYlejq57HnA29a9gT2YeXcON1MX62D3UTnOqh/VdZ9EY76M1uY+jarfyZM8JYDEe6VlFPJZj88AyOobbeL5/Ga9o/RW/7j2B5mQPWwaOomO4lXNb7iFuebb2L2IoX8f+4ZYDiy/jKZ7sPZGF6R1k8kkS+T629bWwsrWDhA3Tl6snFRsm71AXHwQcM9jcfxT1iQG2DCxhcc0Osh5jbqqDezvO5dy2+9g91M4x9S8wmE+RtCz7Mm0kY1nMoSdXT9KyLKndQtaTxMhSEx9m99Bc+rINADzXdwzH1j9Hb7aG+vgAMYOe4To2D67gse5TqY31s6R2K493n8DZrY9gwEAuRc5j1MUzbOpfwvK6LeQ8BmYM5NI0xPuCZBSIxWsgnoaj3gLZXujbBInaYOvjc1+B4c5gATUdD21nQ6IOtt4abNGzFOQGw2xwGHwYLAHJNhjeH2xVy3UFCV/9smDL7CxU1S1pxcxsLdDr7p8uGvdFYJ273xi+fhpYM97uzpnckrZ27Vp6H3+ct2cmuztIRKbC11JpGk49lbVr11Y7FJGDFN6Xen/KZIy3Ja1q/46aWb2ZNRaeA78JPFFS7VbgHeFZnucCXbP2eDQRERGZVaq5u3M+cLMFO8oTwDfc/Udm9i4Ad78GuA24BNgI9ANXVilWERERkRlVtSTN3Z8HTi8z/pqi5w68dybjEhEREYkCHX0rIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCJISZqIiIhIBClJExEREYkgJWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIUpImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQiqepJmZnEze9jMvl+mbI2ZdZnZI+HjI9WIUURERGSmJaodAPB+4EmgaYzyO939DTMYj4iIiEjVVXVLmpktAV4PfKmacYiIiIhETbV3d/478NdAfpw655nZo2b2QzM7eWbCEhEREamuqiVpZvYGYLe7PzhOtYeAZe5+OvA54JYx2rrKzNab2fo9e/ZMfbAiIiIiM6yaW9LOB95oZpuAbwIXm9l/F1dw92537w2f3wYkzWxuaUPufq27r3b31e3t7TMQuoiIiMj0qlqS5u4fcvcl7r4cuAz4hbv/QXEdM1tgZhY+P4cg3n0zHqyIiIjIDIvC2Z2jmNm7ANz9GuBS4N1mlgUGgMvc3asZn4iIiMhMiESS5u7rgHXh82uKxl8NXF2dqERERESqp9pnd4qIiIhIGUrSRERERCJISZqIiIhIBClJExEREYkgJWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIUpImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCJISZqIiIhIBClJExEREYkgJWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIUpImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBVU/SzCxuZg+b2ffLlJmZfdbMNprZY2Z2ZjViFBEREZlpVU/SgPcDT45R9jpgZfi4CvjCTAUlIiIiUk2JanZuZkuA1wP/CPxlmSpvAm5wdwfuNbMWM1vo7jtmMs6oaFiwgxUXPgSAO5iVVHDAxi8ry8PiscpnMfdwsRUtm3wOPB8nnsiVX6ZjLeui8e7gWYglDyWoMdovxJeHWKX/fpW0mc9CLAGeD98Xk5zPDTddTH64tsLOpeq6noKnPgMdD8HC18CSN8Pdb4U558JpH4WN10K8Afo3QawGXvZF6NwAu9bB7tuh6XjYdTuc+S/w3HWw8xcQT0N6AXQ9DhaD498Pe9ZBojUY13I61C2ATDfsuQ8GtkLtAshlYGALWBLazoBUC7RfACf/DWR64LZTofE4OPnvoH8LbPo6NK6Enk3Q/SiQhMWXwJmfDmJ45j9h45dgYDtc8B2Y94qgv/v+GJKNQRtHvwPS86DryWB+hvbC0X8MPb8G4tD3QvDBajoGmk+FwR1w2sdgx48hsx9aV0HLaXDX5ZCo47KFGxjyGnjMYP8jULsQ+l8M4h/aCYt+CzbfCLE0JJuhbjFk9kHbamg+MVjud18WLINUM9QvCz6IiXpoOwdW/gnkh2Hjf0HrmfD4Wuh9IZi3tjOD+dj1C6hbBvkM1CyA3ACk58BwJyx+Mzz9b9CwAgb3BvNHDFpPh8WvD5bLsrfCU/8eLLfTPxnM56avQ9Nx8Ov/B/EaSDRA6xmw/fsw5xzoeBh6noXll8OcsyHVFrwvdv8yWN/nfB6euQY2/Tek2yHZBIM7oWUV9D4TzH/jsbD/8WA5tJ0FT/1bMA8v+xLE4jP/2agyC/KfKnVu9r/AJ4FG4IPu/oaS8u8D/+zud4Wvfw78jbuvH6vN1atX+/r1YxZPqXe961307dvH/Hx+Rvr7xBtvoSY5M32JHIqd3XV8+qevnfZ+dsVi1M+ZwzXXXDPtfVXD9ddfz6ZNm2asv7cu/BYnNj514HXXcAPNyV4AnutbwTH1L4z65+/LL17JBW13cVzDs8DIP4b7M820prrK9lH2n8dJcod/2vghXtd+G2e2PApAT7aehnjfmG1+e/ul/Lr3ZD6y8uPELPid6x6u5zMvfJD3Lf8P2lKd48Y2VryF8Y90nc6q5kdxh55cI+s7z+DiuXdMen7Ga7sj0zIqvmLZfJxPbPwwK+uf5fcXf6Ns3YmW9XA+RjJW/rckm4+TiOX47PN/yvuOvhqAr2x5J8trN3PR3HUM5NLUxocO1M87xMZYdgO5GmpigwdiuXrTe3jPss8fVL/YYC5NTdj+xr5jOLb+OQD+Z/ulbOg9eewJp9Dy5cu54oorZqQvADN70N1Xlyur2pY0M3sDsNvdHzSzNWNVKzPuoKzSzK4i2B3K0qVLpyrEyPnOQ6dz+dkPj1k+3gdzgo0vUoGcQ96NZOzQ/8FxD9pJHMIBBxOty8P5MSzI5SEeq7yta+94xeF1LABs2rSJZ5/cSHNq7oz0d9PuM3nN0TmWNO7hqX1H8fDOY/nDVT9mc2c739t4LhcuqyOTTdBW20M2H+fBDY3sblrNiXPmcmzbdnb3NbOybTvfeeoCXnHU4xzbup1sPkZ3ppb59Z2AcdeLp3DsnO0MDieZX9/Jjr42ugbrqU0OsbRpJ42pQfqGa4lbjob0IPk87OprYyCb5vnORWzb2M+Nm8/m2Fc8zZ7+Jn743LnMqe3mnMVPs6eviZZ0N4sa95Nz4+l9S7l7wzyG853cmj+P8xZvoDE9wFcffhW7Ozq5Yf8afv/UXzA0HKe5ZoD1O46jJ1PPwvq9rGjdRl1ymAe2Hc/Cxv3kHNpqegGnY7CJbd1zaKnp55anz+CZ+S3UJQd5sWs+m7vmsfzMp8lkE2DDDGST7O1v56imPXQNNdBW000yPkxTeoAn9xzFafM3kc0bQ7kU3UO11CeH2Nrdzvbedp7bv4A/WvUjsnnI5NJ0DDYAMTK5OC90LmL3pk46Yq3U9ZzP5s55/NZxv6K1podNXQt4sXseCxs6OKZ1G12DDcRjeXqG6kjEsvQN11CXzPDIzhVctPxROgYaqU8O0FbXS95hZ+9cntizgsFsiqe2xvle7mU0p/t48NdNPJ06lp4lQ+zqa+Y3VjzIcC5OJp/ixc65nDpvMy92z2Nx4x7aant4dNcxbOpaSG+mluPmvMhxbdvY3jOHXz+Z4JbB8zln0VP0ZmoYHE7RWDPA9u422uu72dw9nz19zSxp2sv2njls6lzIbxwdI255bn9sAXnvnPbPQldm77T3UYmqbUkzs08CbweyQA3QBNzk7n9QVOeLwDp3vzF8/TSwZrzdnTO5JW3t2rX0Pv44b88MTVxZRKbM11JpGk49lbVr11Y7lGmxdu1adj/XyQXz31ztUERmlTt33cK8Y1pm9LtlvC1pVTtxwN0/5O5L3H05cBnwi+IELXQr8I7wLM9zga7ZejyaiIiIzC5VPXGgHDN7F4C7XwPcBlwCbAT6gSurGJqIiIjIjIlEkubu64B14fNrisY78N7qRCUiIiJSPRUlaWbWApwKzCM4fnkPsMHdO6Y+NBEREZHZa8IkzczmAn9IcNzYaRx8Ypmb2QbgG8B17r5nyqMUERERmWXGPHHAzFrN7N+ALQTXMjs9rG8ljxhwCvBPwBYz+4yZtU534CIiIiJHsvG2pG0EWhi95exF4BmgIxzfBhwPLAnLUwS3eXoHMDMX+BERERE5Ao2XpLUSHHf2c+BG4AfuvqtcRTNbQHB7p8uBi8JpRUREROQQjZek/TfwSXcf6+bnB7j7TuDLwJfN7GTgb6YoPhEREZFZacwkzd3fcSgNuvsGgt2dIiIiInKIqnbHAREREREZ2yFdzNbMaoDzgPnAduAed89OZWAiIiIis9m4SZqZvSd8+p3CSQNm9tvAF4E5RVW3mtnb3f2O6QlTREREZHaZaEva1QRneD4C7DKzs4BvFU3nBJfiOAr4vpmd6u6bpylWERERkVmj0mPSPkiQoHn4uvgaavXAX0xFUCIiIiKzXaVJ2gUECVoH8DqgEbgE2BeW/+bUhSYiIiIye1WapLWHw3929x+7e5+7/wj4FMFWtWVTGp2IiIjILDXZJK2we7MzHD5SUv7wVAQjIiIiIoHJXoLjajPrAmrD16W3fWoOh2VvGyUiIiIilZnslrRVwIUEJwcAnFZSviYcbjz8kERERERkMkmalXn83oFCs3rgbQS7RH80DTGKiIiIzDrj7u5098kkcSuAz4XPv3XYEYmIiIjIod0Wqpi7PwE8MQWxiIiIiEjoUO/dmQQWArj7i1MakYiIiIhUfJ20gnOATcDzUxeKiIiIiBQcapJWYBNXEREREZFKHW6SJiIiIiLTQEmaiIiISAQpSRMRERGJoEO9BMczwJVTGYiIiIiIjDikJM3d9wBfneJYRERERCR02BezBTCzo4CLANz9hqloU0RERGQ2m5IkDTgTuB7IA0rSRERERA7TVJ84oOumiYiIiEyBcbekmdlHJtnOCVMQi4iIiIiEJtrduRbwGYhDRERERIpM9pg07cYUERERmUETJWmFrWhPAPvGqTcXOHlKIhIRERGRCZO0Z4GVwJfc/bNjVTKzNwE3T2VgIiIiIrPZRGd3PkCwq3P1DMQiIiIiIqGJtqT9FDgFSE9Qby9wBzrJQERERGRKjJukhXcPmPDitO5+N7BmimISERERmfWm+mK2IiIiIjIFqpakmVmNmd1vZo+a2QYz+1iZOmvMrMvMHgkfk724roiIiMhL2phJmpldYWbxShs0s7iZXTmJqkPAxe5+OrAKeK2ZnVum3p3uvip8fLzSeEREREReisbbknYdsNHM/tbMVkzUkJmtMLO/B54D/mui+h7oDV8mw4dOPBARERFh4rM7lwL/CPyjmT0F3Edw7bR9BJfmaAOOB84Jh4TjJ5VshVvqHgSOBf7T3e8rU+08M3sU2A580N03TKZtERERkZey8ZK03wD+FTgtfH0C499IvXDrqEeBD0ymc3fPAavMrAW42cxOcfcniqo8BCxz914zuwS4heDiuqM7NrsKuApg6dKlk+laREREJNLG3N3p7j8HzgAuBX4ejrYxHgA/A34XONPdf1FJEO7eCawDXlsyvruwS9TdbwOSZja3zPTXuvtqd1/d3t5eSdciIiIikTTRddIcuAm4KUyOXgmcCrQTJGe7gceBO9x9byUdm1k7MOzunWZWC7wa+FRJnQXALnd3MzuHIKkc7x6iIiIiIkeEiY5JOyBMwm4KH1NhIfDV8Li0GPBtd/++mb0r7O8agq147zazLDAAXBYmjiIiIiJHtEknaVPN3R8j2J1aOv6aoudXA1fPZFwiIiIiUaA7DoiIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCJISZqIiIhIBClJExEREYmgMS/BYWaHdH8ld3/x0MMRERERERj/OmmbmOSN0ov4BG2KiIiIyCRMJqGyiavgk6wnIiIiIpMw0TFpk028lKCJiIiITKExt6S5u04qEBEREakSJWIiIiIiEVTxQf5m1gy8BTgZaASuAhaHxVvcvdKTDURERESkREVJmpm9Hvga0ExwHJoD7wYeB+qB1wM/nuIYRURERGadSe/uNLNTgf9lJEEDwN0zwPfCtt401QGKiIiIzEaVHJP2ISAdPn+4pOyucHjeYUckIiIiIhUlaWsIdm9+AnhfSdmWcLhoCmISERERmfUqSdLmhMPby5RlwmHLYUUjIiIiIkBlSVp3OFxZpuzscLj/8MIREREREagsSXuI4ISBjwO/VRhpZn8M/C3BrtAHpzQ6ERERkVmqkiTtunA4F/grRm6+/kWgoaSOiIiIiByGSSdp7v4t4EaCrWnF9+osPL/R3b8zhbGJiIiIzFqV3hbqD4D3EuzWHAwfD4Xj3j61oYmIiIjMXhXdcSC85dMXwoeIiIiITJNK7jhwp5m908zqpjMgEREREalsd+f5BCcG7DCzL5rZy6YpJhEREZFZr9Jj0gxoBP4YuMfMHjez95vZnAmmExEREZEKVJKkXQH8GMgycobnScBngG1m9i0ze82URygiIiIyC1VyCY4b3P11wGLg/cC9jCRrKeD3gB9MR5AiIiIis02luztx9z3u/jl3fzlwLHBbUbGNMZmIiIiIVKCiS3AUmNnxwO8DbwNWENx9QAmaiIiIyBSZdJJmZguBywiSszOKi8LhHuBrUxeaiIiIyOxVyZa0LYwkZIVhHvgR8GXgVnfPTmFsIiIiIrNWJUla8fFrzwNfAa53921TG5KIiIiIVJKkDQA3AV9293XTE46IiIiIQGVJ2iJ375q2SERERETkgEknaYUEzcyagbcAJwMNwP8huHYawJbwJuwiIiIichgqugSHmb0BuAFoJjh5wIH3AI8D9cDrCe5KICIiIiKHYdIXszWzU4H/YSRBA8DdM8D3wrbeNNUBioiIiMxGldxx4ENAOnz+cEnZXeHwvMOOSEREREQqStLWEOze/ATwvpKyLeFw0RTEJCIiIjLrVZKkzQmHt5cpy4TDlsk2ZmY1Zna/mT1qZhvM7GNl6piZfdbMNprZY2Z2ZgXxioiIiLxkVZKkdYfDlWXKzg6H+ytobwi42N1PB1YBrzWzc0vqvC7sbyVwFfCFCtoXERERecmq5OzOh4DfAD5OcLcBAMzsj4G/JdgV+uBkGwsv1dEbvkyGj9LLd7wJuCGse6+ZtZjZQnffUUHc02pXLMbXUumJK06B97xyHcvmdBAzcIecGzGcWAzy+WDhBQvQcHeSccg7DOeMZNx5bm8rK+bsJxGm5oPDAHHMIBnPEbPR/RUupuIOsVjQdt4hHvafD8fjwetMzkgnnGwekvGD28m7ETPHDIZzkIgRPM9CIhHUy+YglQjnI+wDIB7GnM/DcD5GPJY/MB95D85kGRo2alJ+oE4qESwUM8jlgzZy+TBuM/oyCWIx6BlI01o/gOGk4vlgCYbLYjgXIxELxnUN1FCTHCaVyNHRV0NzbQYjTzwOvYNJGtLDAOzvT9NcM0Q8PjLvubwxlItTl8qSz0PMgj7y+ZG+CsxG5gkLhnmHbM5IxIPlt6+3loHhNPMaukgl/MC51kO5GKl4nmweDCMRC8oK6yseC6oODMdxj1GbHD6wPmLGgfeAU7TcLJinlrohzIruCZfnQNtm0J9J8fEfvB6nZIamwa5YjIZp72X2WNh0L/MbH6ZvaAHJeC+1yQ4a0i/SVLON3qEFbO8+j8b0VgCe2HElZnnOWvLvzG98CLMs7gk6+o+ntfZZYrEsHX3H05NZRG1yL+31G8h7kl88+xlOXfgVFjStZyDTQl1qPwPDbezpXcXjO97JSQtu5Lm9ryeXT7Hm2L+ma3A5/Zl5mOXY2X0Wx7Z/j86Bo+kdXMSqxddilmN3z6nUpfcxmGmlLr2brZ2vYDjXwNFzf0A2V0s2n8aIkU50AMYze36Hpa138MSOt7O9++UH5j+d2M+pC6+na+AoTl7wdTK5Bvb2nsT8psfY2XM6S5rvZTDbSDbXyN6+E2hveIJkrJ+9/SeTy8dZ0fZzsvk07jHynmT/wNEYRjZfS85T7Ok9nS2dr+QVKz5CfWoXMRvmV5s/xHCunvOW/xMvdlzIcfNuoWdwIY/ueBcdfSdw+uL/Ylvn+aQSnZy+6Mvs719Jb6adFXN+RtfAMpprNhGL5YjbEDlPcv/mv2Ew28KZSz5P31A7HQMn0pDaQV+mnePab6FrcDGtdS/Q0XcMvZmlPLT1z8h7CgAjy+mLr2Fx031s7TqfVLyHhvQ2Ypajd2gROU/z/L5L6Og/gbbaX3POsk8zMNxGz9BS5tZv4O4XPsLCpvs4deHXeG7va1nUfD/1qa3EY3k6B5axuePV1CQ7WND0EAOZdnb3nkZDegedA0fTkN7OkuY7SCX62Nd3Ag3pbaQT3WRzadKJXjbvv5j7X/ybanwsIqGSJO06giRtLvBXjCRUX2TkchzXVdK5mcUJErtjgf909/tKqixm5Hg3gK3huFFJmpldRbCljaVLl1YSwmFZvnz5jPUFsGLuTQd+0M0gVpTTxuPFNYvGG8Rjwevj5o3e0FmbAsiN2V+hr8KPsDHyIx70X6gYthcP+kmVbJ8ttFMcb6ronZdMhHVsJBkrJCilm3rjcYiHiVTxPALUpv3gOmFZIj56CE5zbZBUNYbJVTmpxEhfrfWDB563Nw6OqtdYM3xgPuc0DB0Yf2DeY04yEdzaNla0rkavt4Pnqfh1YT0G/Q8Q3ASkiEFtmFAGy9FHlRV3VZfKUbzuU2N8ExRiLZ6nsWJvSGdYsGoFPbnG8o1NoQZm/vN3JDuu/WYWNd/PULaJRKyfeGzkNswN6V00126hJtGBGeztO5WYZTmq9e6iFoZZ0PTIgVfzmx5jPo8VlQ9wzNzvsbjlHmLmJGt3AdAY30ljzY/oy8xnZfut5PIpjBz16T3Up/ccmLqt7mla616gveEJhoabicezB/oxg6aa4O6Ex827GYCY+YHvrWInzP82tclOTpz/rVFJ2vzGh1nZfit9mXZqU53U0klzbZCUrkjuJR7LUpMMruU+p/7JA+021W4jl08Qj2VJxDMH2qtP7z3w3B3aG55gYLiNBU0PH4jr+PabyeTqaandTN38/yWV6Kcm2c2xwz/gGU9y/LybqE3uoyG1ncaa4JHLJYjHs9QmO0fNV4JhTpj/bfoz7cypf5q2uqeZ3/Qo6UQPQ8NNpJPd1CT3YwYLmx8DHuO5vW9k/0CwY6yxZhsnzv8OACtTt5KMj3y3zGt8IpyPOB39J7By3ndprXuBVl4gm3+cRCzDstZfcszcW6lJdnLcvO+Omn5O/XPUpTqoSQT9e+1ztDc8QSrRS+/QQhrSIz/ni5rXH3ieivdhBivm/FhJ2mS4+7fM7I3A5YVR4bDwMbjR3b9TSefungNWmVkLcLOZneLuTxRVKfcv+UEXy3X3a4FrAVavXj1jF9O94oorZqqrwMaj4JnPQ+cTkG6DxuMg0w29T0PtUki3ArHglzVeC/sfgrqlUHcU9G+F498Lz30JejZBfgjmX0S4zQQ6n4LB7ZAfBosFj3Q7xJOYpaB/M7hB/RLo3Qh1yyA9Bwb3QK4XUu3QeCzsfxAajoe9d4HFIVYDiRQMD0DLCTCwC4b2QusZMLgLup/ElrwROh+DWAoaT4AdP4KaeZCoD6evhX33Q6oNGpZD7SIY2A29z4bZYkPQR/OpsOcOSM2F1lNh9+1AGoa2QcsZQdxNxwfLJtvPA88NErc8Z154Gey5GywJ+9YDWRjcDfEGaD8XBnZApgOWXQa9W6D7CVjyO7DvXvAc9D6PLfkdePFGSLbAkjfC7ruh56lg81WyDhqOgcYTYet3oHElDO2G4T5INY+sg/5tYIlg/SXmBHHE4zCwPVjXNfOhbzNkuuCYK4Plv/OX0PMs5DPBpq25Z0PfRkgvAM9D54ZgXcZrINkMA9sgn4V5r4BkE+z4cbA+2i+CXHewfvKDkJ4XvGeaT4GBrbDsLbDlu8H7ZmBXsAm1fnnwOtUCQ/ux+Wv4wO//68x+JmRK3P/iB2mu2czAcBvx2DDpxH7Sif201LxAz9AS9vadTG1yH/FYhu1d5wHOcK6GpS2/BIZxT7Oj52XMa3iAVGKAF/dfyHCumXSii4VNv2I4X8dj269iZ/dZHNf+XXb1nsy8+g10DKyko/9ktnW9nP0Dx7K7dxXZXDrYStZ7GnlPk88n6B5azqLme+gaWEZ/pp2zl36aZLyPp3a9lZa6TfQMLaQ5vZnt3eeTy6dZ2vpThrIt5D0JxKhJ7MMsy3P7fouFTQ+wZf9Fo+Z/y/4LuSOforN/BScu+DoDw+3s7T2FhU0PsL37XJa3/YTeoYUM5+vp7D+WOfVPEo/10zWwksFsMycvuIH+zDwyuRoSsTy7e08jEcsEW9LySXozS+geXMoDL76fdKKDdKKXJ3a8k1w+zeDwHF7oeDUnL/g6e/tOYEvnxQxlW1m38Z/Z378SY5jj5t1MR/9x9A4uZOW8W9jVs4q59U8Qsyw1yU5y+TQPbX0/TpyO/uPpzSygb2gR9ald9AwuZOW8m9ndcypLWu5lZ89ZDGTaDyRoAN2Dy/jFs59mXsMjbO86l1Sim7rkbsyy9A4tIh7OE8BDW/+M3qHF9Gfa6BlaQnvDEzy9+y1s2X8BJy74Bs/ueTMLmh6itfbXpBM9bO8+j53dZ5OMDzCv8WF6BxfTPbiM+vROugeXUpfaw7yG9dQl9rKr7wwa09upT+2kP9NGW91zPLX7rTP3QYggq+QGAWZmwLuAPwROCkc/CXwZuOZw7jZgZh8F+tz900Xjvgisc/cbw9dPA2vG2925evVqX79+/VjFIgesXbt21FAkKtauXcvu5zq5YP6bqx2KyKxy565bmHdMy4z+LpjZg+6+ulxZJScO4IEvuPvZ7l4fPlaH4ypK0MysPdyChpnVAq8GniqpdivwjvAsz3OBrigdjyYiIiIyXSq6LdQUWwh8NTwuLQZ8292/b2bvAnD3a4DbgEuAjUA/cGW1ghURERGZSWMmaWb2i0Noz939VZOs+BhwRpnx1xQ3Brz3EOIQEREReUkbb0vaGsocpD+OwhmeIiIiInKYJtrdOf0XPBIRERGRg4yXpOn4LxEREZEqGTNJc/evzmQgIiIiIjKioktwiIiIiMjMUJImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhE06Xt3mtlHwqfXufvWkrIG4DQAd79n6sITERERmZ0qucH6WoLbPv0M2FpSdjpwJ5CvsE0RERERKWOqdncWEjPdRkpERERkCoy71cvMlgLLS0afYWbF08WAd4bPdYN1ERERkSkw0a7JK4GPFL024LPj1N9z2BGJiIiIyKSOHyvdhTneLs2bDyMWEREREQlNlKR1ApvD58sIdmfuAoaK6uSBfcBPgU9McXwiIiIis9K4SZq7/wfwHwBmlg9HX6rLbIiIiIhMr0ldLsPMksB3CbakvTitEYmIiIjIpC/B4cAbgTcBr5m+cEREREQEJpmkuXsW2Bu+3DxeXRERERE5fJVczPaHBGd2njNNsYiIiIhIqJIk7W+BZ4EPm9l7zGzBNMUkIiIiMutVkqRtA44FaoDPAdvMLFfyyE5LlCIiIiKzTCU3QzeCEwi86LWIiIiITINKkjRQYiYiIiIyIypJ0lZMWxQiIiIiMsqkkzR316U3RERERGZIpbs7MbPFwGXACeGop4Bvuvu2qQxMREREZDarKEkzs7cDXwTSJUX/YGZ/4u5fn7LIRERERGaxSV+Cw8zOBL5MkKBZyaMGuC6sIyIiIiKHqZLrpH2AYMubAduBW8JHYTdnAvjLKYxNREREZNaqZHfnBQTXSLsFuMzdhwHMLAF8C/ht4MKpDlBERERkNqpkS9r8cPilQoIGB26+/qXw5bypCkxERERkNqskSRsIh0eXKTu6pI6IiIiIHIZKdnf+GjgX+KdwF+fd4fjzgbUEu0J/PaXRiYiIiMxSlSRp3yJI0hqAz5SUFe7r+c0piktERERkVqtkd+fngfsYuX9n8SU4AO4N64iIiIjIYZp0khaeLPAq4N+BjqKifcC/Ab8RnkQgIiIiIodpzN2dZvbK8OnD7t4D4O79BNdC+0szawfM3XdPf5giIiIis8t4W9LWAb8ETgUws7yZZc3s5QDuvudwEjQzO8rMfmlmT5rZBjN7f5k6a8ysy8weCR8fOdT+RERERF5KJnPiQHEdG7NW5bLAB9z9ITNrBB40s5+6e+kZone6+xumsF8RERGRyBsvSesF6oG/MLMVReNfZ2bHjjWRu98wmY7dfQewI3zeY2ZPAovRZTxERERExk3SnifY1fnG8AHBlrS/G2caByaVpBUzs+XAGQRnj5Y6z8weJbhf6AfdfUOl7YuIiIi81Ix3TNrVHHyZDUrGlXtUxMwagO8Af+7u3SXFDwHL3P104HME9w0t18ZVZrbezNbv2bOn0hBEREREImfMLWnu/iUz6wDeTLAb8iKCLWWPAl1T0bmZJQkStK+7+01lYuguen6bmX3ezOa6+96SetcC1wKsXr3apyI2ERERkWoa98SBMHG6CYKzO8PRf+ru9xxux2ZmwJeBJ9299A4GhToLgF3u7mZ2DsGWv32H27eIiIhI1FVyW6grw+EzU9T3+cDbgcfN7JFw3N8BSwHc/RrgUuDdZpYluHn7Ze6uLWUiIiJyxJt0kubuX53Kjt39LiY4hs3dryY4Nk5ERERkVqlkSxpm9jLgfcBxQBsHJ1nu7sdMUWwiIiIis9akkzQzexvwtfGqEJxYICIiIiKHqZItaR9mau84ICIiIiJjqCRJO5pgS9mvgH8D9o5fXUREREQOVSVJ2haCRO0f3P3H0xSPiIiIiDD+HQdKFe5A8LppikVEREREQpVcguM/zOx44M/Cszx/BXSWqffxqQtPREREZHaq5OzOZcBrCLamnRM+ylGSJiIiInKYKjkm7XPACoKTB8Y6y1OX4BARERGZApUkaWsIkrB+4HaCe2jmx5tARERERA5NJUnaIFAP/Im7f3Oa4hERERERKju78zvhMD0dgYiIiIjIiEq2pH0euAj4VzNLE5zd2VVayd1fnKLYRERERGatSpK0Rxk5aeALY9TxCtsUERERkTIOJaEqnMFpJeN0X08RERGRKVJpkjZWIqYETURERGQKVXLHgUpOMhARERGRw6DES0RERCSCKj4mzcxWA+8ETghHPQV81d3XT2VgIiIiIrNZRUmamf0d8A8loy8G3mNmf+/u/zxlkYmIiIjMYpPe3WlmFwOfKLws8/hHM1szxfGJiIiIzEqVbEl7Xzg0YD1wV/j65cA54fP3A+umJDIRERGRWaySJO1lBNdD+5K7/5/iAjP7IvAnwLlTGJuIiIjIrFXJ2Z1zwuFNZcoK49oOLxwRERERgcqStJ5weFaZssK43sMLR0RERESg8nt3rgE+YmbtwN3h+POBdxHsCn10SqMTERERmaUqSdK+SpCkJQlOInhfUZkRJGlfnbLIRERERGaxSe/udPevAt9l9GU3KBreEtYRERERkcNU6W2hfhf4c+BhYAAYDJ+/H/i9KY1MREREZBar6I4D7p4HPhs+RERERGSaTJikmVlT+LTf3bNlyhNAHYC7d09teCIiIiKz07i7O83sDcB+YBewfIxqy4HdQEdYX0REREQO00THpL2V4MSAm919Y7kK4fj/Ddt669SGJyIiIjI7TZSknUVwaY0fTlCvUF7uQrciIiIiUqGJkrTF4XDLBPW2ldQXERERkcMwUZJWOLGgeYJ6hZMLKjpbVERERETKmyhJ2x0OXz9BvcIJA7vHrSUiIiIikzJRknY/wYkDV5pZ2YvVmtnvAlcSHLt2/9SGJyIiIjI7TbR78hsEdxKIAd80s6uAnwD7gDnAq8NH4d6dX5++UEVERERmj4mStFuBO4ELwtcXh49ihQTtDne/dWrDExEREZmdxt3d6e5OsCXtsTGqFG6u/hgVXiPNzI4ys1+a2ZNmtsHM3l+mjpnZZ81so5k9ZmZnVtKHiIiIyEvVhDdYd/fdwHnA3wPPECRmhcfTwIeA88J6lcgCH3D3E4Fzgfea2UkldV4HrAwfVwFfqLAPERERkZekSV0yw90HgE8CnzSzOqAF6HT3/kPt2N13ADvC5z1m9iTBddZ+XVTtTcAN4Ra9e82sxcwWhtOKiIiIHLEqvq5ZmJgdcnJWjpktB84A7ispWszoC+luDcfNziTtx+fBvnvDFwkgC7E0pOfAwPZwvEG8DnL9QVl+iOCQwcKhgwUxiNdCbhDIlXQUA/JhN42QbIbBHeDF9YraS8+D3ABke8oEHcZpKfDhkhiK2mo4AXqfgVgyqOelMQHJVhjeXzJpWB8gVhPEnh8Y6WfR62HN96FvM3z3mFHz+tGV4ZNvfKxMTOOwFMTiQRf5AQ5etnEOWqb1x0DfcyVx5w+ul2yD4Y5CJaiZB4O7D27fDDwbvk7B8sth89eLxgG1iyGzP2gnlgrWdX7g4PlJz4OhchvCLZyXsM1kK2R7w3WTP7j6RT+Bhb9Rph2p1M6dO+nO9HLnrltmpL//e8ENLKgf+WwV3m2Fd3bnQD1NNf3EzA8c41KsY6CBttpeALL5GD1DNbTU9pPLxUjE87gHb9liwzkjGR95X+fDp4V6FrY1MJyiMT04atqcG12DdbTV9o0en4d4uH9oOG/EDAwnVtS3h39K4wHKxlk8nZV5PpyPk4jlRsrCwo7+BvqydSxu2H0gpo0dC1jQ0ElDavT8jMRvxGM+aln0DqVpTA+Rd0bNR2lsng/qF+I/8K1fMk+9mRSJmGOWJxXPkcvHSMTyZdsvns/xZHNgsRgxyx+on8klSMSymAXrNj5W7A6OkXdIxIIZz7uF03jwVQd84s7fZ2ff3ElEc3g6M3vJ7yy/fqqh6hefNbMG4DvAn7t7d2lxmUkO+pUPzzq9CmDp0qVTHmNk7H+k6EX4w5kfKkrQABxy4RdXfnD0+FHyI/UOUvQDnO0ZI/kqaq/sD3xJnJ4Zp45D75Nh12WSs4LSBA1GEjQomd/Q3nuCYffTHJyMjv2FPC7PlDRVumzLzENxggaj4y52IEEL2x3cVaZSrqTLDOz6+egEDWBgBwfW5ZjrmnHWn3Ng/UH55V9szz1K0l6i5td3jvosjPpYOLTU9h34AS+XyLTV9h4Yl4jlaa0N/o9PxPMjSVfJ9Mm4j2onXlReqJ+I5WlMDx7UX8Kc1tq+g8bHYyP9JGPhD3zY34G4HWyMA31K2yue1+Ki4ufJWG7UtIVha20vc2O9o9o7qnkvNYmSz+mo+IOYi8NrTA8dGDdWYhkz8FhJuY9O2kbay4xaxhbLj9l+ceI5XlKbTEDpP26peHakjzFiL4iZEytaTzELEuviZb+sedeMJGlRU9UkzcySBAna1939pjJVtgJHFb1eAmwvreTu1wLXAqxevbrcppojwxufhe0/g87H4KjLYd8d0HYm1LTDnvuDH/T65dB0NOx/DJqOgYF9sOcuWPgq6Hoq2LqWnAOJWLClZbgP+rfCcDfkhqH7CVjxB7DnPqidB22rIF4D/btg9+1BHMkmaDopSIBaToKWUyCfDRKhno2QHYJkDaRaofUs2PcAzDkL+nfCwOagvWQTdD4ZxFDTCvNeAbvvgYZjYWAL9LwAPc9B3YKgTn4Q2i+AffdB9zMQr4dEGtpfGYzL9cL8VwWJysAOGOqATCecEJ6PsvA34RU3Q+9zMLgXatr54Q9/yJzUPl62+mxoOQG6N8LgdqhZGMTXuBJ6X4Ch/dD3PNSvAMvD0X8I2c5gS2THY5Bug74Xg62OgztgztmQ2Rckz4nWYJolb4Yn/gmygxBPwNHvDNZXz7NBLjS4F5L1sOIdsPlb0PNMME3j0bD3IejbBM0nwd67YdFrgn+bO58ItmrNPRvmngc7fgw718FwPzQfB8t/H7qeCJZTvB7yGdj2k6DPmnbI5aB5RdDPzp8AaYjHIZuB7ieDdVYzH3bdHWzlPP0foOtxIAl77weGg76GdsC8i+C4/zOjH4cj2YIFC4j1dXLB/DfPSH/f33A2S5ruIBXvIOe19GQWYh6jLrWL3swCugaOo6FmK6lYFzHLU5vczWC2jUSsj3w+yY7eC1jUdBdmWTp6T2A430xb/VN0DKxgRctP2NF9Ds21m6lJ7qNncDG1yS529qymre4pYrF+4uZ0Dq4kboPEbQh3oybZQffg0fRkFrO05SfUJvfj7jgJdvWcS+fAUpa2/pR59Y8ynK+jc+B49g+sZGnLz8nma9m0/zXUpTrI5hLMb3iIweE5JGL99AwtJ2bDtNQ/TS5bg1uO+uReBobn0Dm0kppEF8n4fpI2yFCukYQNkvV6BrLzqEnsI5XoZl/fiSxv+SHbu17JQK6dRLyXpS0/ZWC4nX29p5GID7C790xisTx1yW3Ma3yQbK6ejXsvpT65ndb6p6lL7cLzzoLG++gYOJHB4bns7j2dhU3r6Ro8BncjFhuma2AFCxrvp2dgMXManiLvMVKJHrL5OhrTm9nXexo50vQNzSeR6GNO7a8xnK7BY2iqfYF9fSexpPlO+jOt9GWW0TV4HDnAHNrqnmFv34kcPecHbNp/EfMbHsM9QdbTeD5JzuOkE71k8zXUpXaQzaZorNlOJldHJtuMxSCbr6N7cAXuMVKxbpLxARpqN7Fpz2tpqN1JKtbLUL6JBQ33MpCZQ3Pt8/RlFtA7tJS5DRvY2nUu8Rj0D81lYdN9DAy3M5hrw/MpkrEO6pK76B0+ilTu1Vwwf/o/C3fuuoV5C1qmv6NJMvfq5DRmZsBXgQ53//Mx6rwe+FPgEuBlwGfd/Zzx2l29erWvX79+iqOVI9HatWtHDUWiYu3atex+buaSNBEJ3LnrFuYd0zKjvwtm9qC7ry5XVs0taecDbwceN7NHwnF/BywFcPdrgNsIErSNBMfBXTnzYYqIiIjMvKolae5+FxMckxie1fnemYlIREREJDomvE6aiIiIiMw8JWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIUpImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCJISZqIiIhIBClJExEREYkgJWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIUpImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiaCqJWlmdp2Z7TazJ8YoX2NmXWb2SPj4yEzHKCIiIlItiSr2fT1wNXDDOHXudPc3zEw4IiIiItFRtS1p7n4H0FGt/kVERESiLOrHpJ1nZo+a2Q/N7ORqByMiIiIyU6q5u3MiDwHL3L3XzC4BbgFWlqtoZlcBVwEsXbp0xgIUERERmS6R3ZLm7t3u3hs+vw1ImtncMepe6+6r3X11e3v7jMYpIiIiMh0im6SZ2QIzs/D5OQSx7qtuVCIiIiIzo2q7O83sRmANMNfMtgIfBZIA7n4NcCnwbjPLAgPAZe7uVQpXREREZEZVLUlz98snKL+a4BIdIiIiIrNOZHd3ioiIiMxmStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCJISZqIiIhIBClJExEREYkgJWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIUpImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCJISZqIiIhIBClJExEREYkgJWkiIiIiEaQkTURERCSClKSJiIiIRJCSNBEREZEIqlqSZmbXmdluM3tijHIzs8+a2UYze8zMzpzpGEVERESqpZpb0q4HXjtO+euAleHjKuALMxCTiIiISCRULUlz9zuAjnGqvAm4wQP3Ai1mtnBmohMRERGprkS1AxjHYmBL0eut4bgd1QknAn55Cez4YclIA0uBZ4Ln5IuK4tB0MnQ9Bs2nwEU/gVuWFNWJBc9jacAhnw3HB+Xu8EL/cgbytZzU8OSYYTngxBjMpamJDZHxJN/e/hbOa72HY+ufZ/9wM23JzlHT7Mu0Mie1f1KzvW+4lQRZmpM9AOwcbGdBzR4cA3fMwDEMPzBNJp/AiZGMZQ78J+LhEurP1RKzPB9dOQTAwNc+xWA2TXOqCxsjhpxDvKSwuM9MPkYqlh9Vngf6sg3UxfuJ2+iy8n3EytbLAzmPk7TcgXGDuTQ54tTF+8mH0xXmr9AW5IkbmIXrucBSkGyCzF6Cr4AczH057L03eF4Qr4E3bYF73gY7fw7nfwN+9UeQ7ytUGF0fg4t/DgsumnBeZWJdmb3cueuWGenrL8/5Jse07Zq29t3BxvpwFcnkYqTi439WJlNnVN8w5uc674aZj1k+3rSzVbnvwvHkPfg+SsbKr7OuwVqaawYmbOfT917KC52LJ9/xIerK7GUeLdPez2RFOUkr9zbwMuMws6sIdomydOnS6YypurK9ZUY6eJZCqjS6KAfDfSPT+jCjfqwLz310clYsFcuQn2CDqwFGnrhlMcuTsmESsSzp2DCGk7TsQdOUGzeWtGWIFSUoyVg27NcPvEusZN7jlsPxUZEX3lCJWBYrqp6KZcjFY+N+GcfKFBb3mSiTXBmQjGUmlaAFfZR9ewfzYLlR4+KxHDHPH1j2hf7Kt1XSv2chPxS+CNdDrp/RCRdB0u7Z8H2Xh2xf0XRl2sUhP1h2HqQyy5cvn9H+6urKv/emymQSNID4GJ+BUXVilcU6XtfjJWgTTTtblfsunKi+jfMdmExO7vtx3pIa+ua0VNb5IZhHy4x//sZj7tP74Ry3c7PlwPfd/ZQyZV8E1rn7jeHrp4E17j7ulrTVq1f7+vXrpyPcaMjlgm+8fB7i8WBcYR0WxheeA8RiwTSFuvl8UFYYV/wvbuF5oU5h+sJ0BWaQDX/cC+2WfgsXXufzQRv5/EjMhWFhXgCGhyGVgqGhYFgcs9lIn7HYyDzFYkHM7qOfF+oXFOanMG+x2MgyKK1b6KM4fndIJEbaL55Hs4OXW3F/pcuhePrCMi2MLwyz2dHrq9B3oe3SeIrnq7Bcyi3/QqyF8kI/xX0XtxOPl48/mw1ep9Mj8RTPg7w0DQ+PfC6K31+F55lMsH6TSRgcDNZ/LkzsC9O5j37fFN57hc97Nht8vgcHg3ZK3zeF92jx5z+XG3n/xWLB+OHhkbYL/WSzI7EW5PNBfwMDwXSF937h81D8OSt8dkq/M4vnsfgzUtovBM8L7adSo2Mp9FMYFtovVfjeGB4OllGh3cJ0icRI/0NDQTuF5VtY9sXfVYXvtGRypP/S743C6+JhQWGdFpZDIhG0Wfq9YTayfAvrsDjWQr3ivguxF5ZbQWFdFK+TsZbXEcDMHnT31eXKorwl7VbgT83sm8DLgK6JErRZofBGLf4xLH5zl3sjF48rTFf4UimetvC8XBulP76FL4+JFH+ZFZ4X2i/uJ50OhjU15fsrjrl42nLxlyruZ6znBaXzNdZyLla63MrFXi7ecvXMgi/3saYrjrk4CSyMT5R8pMst64LS90Dxj1ZpO8U/osXLaDLLR14aCuu1dN1DsG4Ln02A2tpgWLz+y01XXCeRGKlTVzd2HIUf+9Lpy8U60ee5NN6x6haShXJ9TvSPx2RjmEx5scJ3Ioz+TihWPF9jtV3ue/NQ4ileDpP5/i99P5Su1/HqjvU9NAtVbQmY2Y3AGmCumW0FPgokAdz9GuA24BJgI9APXFmdSEVERERmXtWSNHe/fIJyB947Q+GIiIiIRIoOIBERERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCFKSJiIiIhJBStJEREREIkhJmoiIiEgEKUkTERERiSAlaSIiIiIRpCRNREREJIKUpImIiIhEkJI0ERERkQhSkiYiIiISQUrSRERERCLI3L3aMUwpM9sDbK52HPKSMRfYW+0gROSIo+8Wmaxl7t5eruCIS9JEKmFm6919dbXjEJEji75bZCpod6eIiIhIBClJExEREYkgJWky211b7QBE5Iik7xY5bDomTURERCSCtCVNREREJIKUpMmsZGavNbOnzWyjmf1tteMRkSODmV1nZrvN7IlqxyIvfUrSZNYxszjwn8DrgJOAy83spOpGJSJHiOuB11Y7CDkyKEmT2egcYKO7P+/uGeCbwJuqHJOIHAHc/Q6go9pxyJFBSZrMRouBLUWvt4bjREREIkNJmsxGVmacTnMWEZFIUZIms9FW4Kii10uA7VWKRUREpCwlaTIbPQCsNLMVZpYCLgNurXJMIiIioyhJk1nH3bPAnwI/Bp4Evu3uG6oblYgcCczsRuBXwPFmttXM/qjaMclLl+44ICIiIhJB2pImIiIiEkFK0kREREQiSEmaiIiISAQpSRMRERGJICVpIiIiIhGkJE1EREQkgpSkiYiIiESQkjQRERGRCPr/tWMlgK6xU2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def udf_box23plot(p_load, p_x, p_y, p_hue, p_palette, p_title, p_xlabel, p_ylabel, p_save):\n",
    "    ax=plt.figure(figsize=(10,8))\n",
    "    if   (p_load==\"one\"):\n",
    "          ax = sns.boxplot(y=p_y, palette=LSEpalt2)\n",
    "    elif (p_load==\"two\"):\n",
    "          ax = sns.boxplot(x=p_x, y=p_y, palette=LSEpalt2)\n",
    "    elif (p_load==\"hue\"):\n",
    "          ax = sns.boxplot(x=p_x, y=p_y, hue=p_hue, palette=LSEpalt2)\n",
    "          plt.legend(loc='upper left')\n",
    "\n",
    "    ax = sns.stripplot( x=p_x, y=p_y, color=\"orange\", jitter=0.3, size=2.0)\n",
    "    plt.title(p_title, size=24)\n",
    "    plt.xlabel(p_xlabel, size=18, fontweight='bold')\n",
    "    plt.ylabel(p_ylabel, size=18, fontweight='bold')\n",
    "    if  (len(p_save) > 0):\n",
    "        plt.savefig(p_save)\n",
    "    else:\n",
    "        plt.show()\n",
    "    plt.close()\n",
    "\n",
    "# The file is blank, so we display the graph and don't save the fig to disk.\n",
    "v_save= \"\"\n",
    "\n",
    "udf_box23plot(p_load=\"two\", p_x=df1_c[\"ClusterNum\"], p_y=df1_c[\"Age\"], p_hue=\"\", p_palette=LSEpalt2, p_title=\"Customer age\", p_xlabel=\"\", p_ylabel=\"Age (years)\", p_save=v_save)\n",
    "udf_box23plot(p_load=\"two\", p_x=df1_c[\"ClusterNum\"], p_y=df1_c[\"Gender\"], p_hue=\"\", p_palette=LSEpalt2, p_title=\"Customer gender\", p_xlabel=\"\", p_ylabel=\"Gender\", p_save=v_save)\n",
    "udf_box23plot(p_load=\"two\", p_x=df1_c[\"ClusterNum\"], p_y=df1_c[\"ComfortDigital\"], p_hue=\"\", p_palette=LSEpalt2, p_title=\"Comfort with technology\", p_xlabel=\"\", p_ylabel=\"Comfort level (1-5)\", p_save=v_save)\n",
    "udf_box23plot(p_load=\"two\", p_x=df1_c[\"ClusterNum\"], p_y=df1_c[\"KnowledgeFM\"], p_hue=\"\", p_palette=LSEpalt2, p_title=\"Knowledge of financial markets\", p_xlabel=\"\", p_ylabel=\"Knowledge level (1-5)\", p_save=v_save)\n",
    "udf_box23plot(p_load=\"two\", p_x=df1_c[\"ClusterNum\"], p_y=df1_c[\"FrequencyTransact\"], p_hue=\"\", p_palette=LSEpalt2, p_title=\"Frequency of transactions\", p_xlabel=\"\", p_ylabel=\"Frequency of transactions (per month)\", p_save=v_save)\n",
    "udf_box23plot(p_load=\"two\", p_x=df1_c[\"ClusterNum\"], p_y=df1_c[\"RiskAversion\"], p_hue=\"\", p_palette=LSEpalt2, p_title=\"Risk aversion\", p_xlabel=\"\", p_ylabel=\"Risk aversion level (1-5)\", p_save=v_save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some variables such as gender, comfort level with digitial knowledge of financial markets, frequency of transactions, and risk aversion) the box plot is useful while the jitter plot does not provide meaningful information. Why do you think is this the case?\n",
    "\n",
    "Answer: An effective tool for displaying the distribution of data is a box plot. It displays a dataset's median, quartiles, and outliers. The data can be examined for trends and patterns using this information. When there are few data points, a jitter plot, a form of scatter plot, is used to show how the data are distributed. Each data point receives a small amount of random noise, which aids in identifying overlapping points.\n",
    "\n",
    "Next, I will attempt to combine some of the visuals into a single plot for even easier visualisation. Note that the variable, `Age`, will be in a plot of its own and all the remaining variables will be grouped into a second plot. This is in order to ensure that variables on vastly different scales are analysed using separate plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def udf_violin_plot(p_y, p_title, p_ylabel, p_xticklabels=[''], p_xticks=[1]):\n",
    "    fig, ax = plt.subplots(figsize=(10,8))\n",
    "    # Create the boxplot\n",
    "    violin_parts  = ax.violinplot(p_y, showextrema=True, showmeans=True, showmedians=True)\n",
    "    # Color all the violin statistics:\n",
    "    for partname in ('cmeans', 'cmedians', 'cbars','cmins','cmaxes' ):\n",
    "        vp = violin_parts[partname]\n",
    "        if    (partname=='cmeans'):\n",
    "              vp.set_edgecolor(purp)\n",
    "              vp.set_linewidth(2)\n",
    "        elif  (partname=='cmedians'):\n",
    "              vp.set_edgecolor(pred)\n",
    "              vp.set_linewidth(3)\n",
    "        else:\n",
    "              vp.set_edgecolor(pgrey)\n",
    "              vp.set_linewidth(2)\n",
    "    # Color the violin body and border:\n",
    "    for vp in violin_parts['bodies']:\n",
    "        vp.set_facecolor(blue)\n",
    "        vp.set_edgecolor(blue)\n",
    "        vp.set_linewidth(1)\n",
    "        vp.set_alpha(0.5)\n",
    "    # add title and axis labels\n",
    "    ax.set_title(p_title, size=24)\n",
    "    #ax.set_xlabel(p_xlabel)  ## 'x-axis'\n",
    "    ax.set_ylabel(p_ylabel, size=18, fontweight='bold')\n",
    "    # add tick labels\n",
    "    xticklabels = p_xticklabels\n",
    "    ax.set_xticks(p_xticks)\n",
    "    ax.set_xticklabels(xticklabels)\n",
    "    # add horizontal grid lines\n",
    "    ax.yaxis.grid(True)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_y = [df1_c.Age]\n",
    "v_xticklabels = ['Age']\n",
    "v_xticks = [1]\n",
    "v_title = 'Violin plot of customer age'\n",
    "v_ylabel = 'Age (years)'\n",
    "udf_violin_plot(p_y=v_y, p_title=v_title, p_ylabel=v_ylabel, p_xticklabels=v_xticklabels, p_xticks=v_xticks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_y = [df1_c.Gender, df1_c.ComfortDigital, df1_c.KnowledgeFM, df1_c.FrequencyTransact, df1_c.RiskAversion]\n",
    "v_xticklabels = ['Gender', 'ComfortDigital', 'KnowledgeFM', 'FrequencyTransact', 'RiskAversion']\n",
    "v_xticks = [1,2,3,4,5]\n",
    "v_title = 'Violin plots of multiple scored variables'\n",
    "v_ylabel = 'Score (1-5)'\n",
    "udf_violin_plot(p_y=v_y, p_title=v_title, p_ylabel=v_ylabel, p_xticklabels=v_xticklabels, p_xticks=v_xticks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Addressing highly correlated variables\n",
    "\n",
    "Highly correlated variables can impact the clustering solution. The following lines of code attempt to decipher whether any correlation exists between customer age and knowledge of financial markets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of age versus income.\n",
    "ax = plt.figure(figsize=(10,8))\n",
    "ax = sns.regplot(x=\"Age\", y=\"KnowledgeFM\", data=df1, marker=\"+\", color=pred )\n",
    "plt.title('Relationship between age and knowledge of financial markets', size=18)\n",
    "plt.xlabel('Age (years)', size=18, fontweight='bold')\n",
    "plt.ylabel('Knowledge of financial markets (1-5)', size=18, fontweight='bold')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there evidence to suggest a strong correlation between age and knowledge of financial markets? With this in mind, what should be done if the two variables are highly correlated? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, there is evidence that there is a strong correlation between age and knowledge of financial markets. One way it can be treated is to discard one of the highly correlated variables from the analysis. As k-means clustering is often an iterative procedure, it is advisable to repeat the analysis without one of the variables and determine whether this makes any meaningful difference to the output. Ideally, if we were to repeat the clustering analysis without one of these variables, we would still obtain similar results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WHAT RECOMMENDATION FOR PRODUCT OFFERING AND RECOMMENDATION FOR MARKETING COMMUNICATIONS WILL I GIVE EACH CLUSTER? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To optimize product offerings and marketing communication for Cluster 0 customers, ABC should focus on offering digital and growth-oriented investment products. AI-assisted services and personal financial advisors could be used to provide tailored recommendations based on their transaction history. Marketing communication should emphasize convenience, ease of use, and potential returns. For example, offering a mobile investment app with a user-friendly interface, providing regular investment insights via social media channels, and hosting financial education webinars. Another product offering recommendation for this cluster would be to provide a platform for fractional investing to encourage investment even with small amounts of capital.\n",
    "\n",
    "To cater to the needs of Cluster 1, ABC should offer traditional banking services with a personal touch, like personal financial advisors to provide customized solutions. Lower-risk investment products with lower returns would be a good product offering recommendation. For marketing communication, traditional channels like print and email should be used, and educational resources should be offered to increase comfort levels with digital platforms. Loyalty programs for frequent use of traditional banking services could encourage higher transaction frequency. ABC should also partner with financial education or retirement planning organizations to offer specialized services to this segment.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}