MSDS422_A3_Evaluating Classification Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Portuguese bank wants its clients to invest in term deposits, which are an investment such as a certificate of deposit. The interest rate and duration of the deposit are set in advance. A term deposit is distinct from a demand deposit. The bank is interested in identifying factors that affect client responses to new term deposit offerings, which are the focus of the marketing campaigns. Regarding the management problem, imagine that you are advising the bank about machine learning methods to guide telephone marketing campaigns. Which of the two modeling methods would you recommend and why? And, given the results of your research, which group of banking clients appears to be the best target for direct marketing efforts (similar to those used with previous telephone campaigns)? \n",
    "\n",
    "Use three binary explanatory variables relating to client banking history: default, housing, and loan. Predict the binary response variable: Has the client subscribed to a term deposit? Use all banking client observations with complete data for these study variables. Employ two classification methods: (1) logistic regression as described in Chapter 4 of the Géron (2017) textbook and (2) naïve Bayes classification. Evaluate these methods within a cross-validation design, using the area under the receiver operating characteristic (ROC) curve as an index of classification performance. Python scikit-learn should be your primary environment for conducting this research."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summay of data**<br>\n",
    "\n",
    "Categorical Variables:\n",
    "\n",
    "- job : admin,technician, services, management, retired, blue-collar, unemployed, entrepreneur, housemaid, unknown, self-employed, student\n",
    "- marital : married, single, divorced\n",
    "- education: secondary, tertiary, primary, unknown\n",
    "- default : yes, no\n",
    "- housing : yes, no\n",
    "- loan : yes, no\n",
    "- response : yes, no (target feature)\n",
    "- contact : unknown, cellular, telephone\n",
    "- month : jan, feb, mar, apr, may, jun, jul, aug, sep, oct, nov, dec\n",
    "- poutcome: unknown, other, failure, success\n",
    "\n",
    "Numerical Variables:\n",
    "\n",
    "- age\n",
    "- balance\n",
    "- day\n",
    "- duration\n",
    "- campaign\n",
    "- pdays\n",
    "- previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import dependencies \n",
    "\n",
    "# Data preparation and analysis\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from math import sqrt  # for root mean-squared error calculation\n",
    "import itertools\n",
    "from scipy import stats as st\n",
    "import random\n",
    "\n",
    "# Modeling routines from Scikit Learn packages\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_curve, classification_report, recall_score, f1_score, roc_curve, auc, roc_auc_score\n",
    "from sklearn.base import clone\n",
    "import sklearn.utils.validation as val\n",
    "from sklearn.utils import resample\n",
    "from sklearn import preprocessing  # feature transformations \n",
    "from sklearn.compose import ColumnTransformer   # for scaling particular features\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, StratifiedShuffleSplit, train_test_split, cross_val_score, cross_val_predict, GridSearchCV \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Plotting and visualization\n",
    "from yellowbrick.model_selection import CVScores\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.features import Rank2D\n",
    "import matplotlib.pyplot as plt  # static plotting\n",
    "import seaborn as sns  # pretty plotting, including heat map\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed value for random number generators to obtain reproducible results\n",
    "RANDOM_SEED = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation and initial inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4521, 17)\n"
     ]
    }
   ],
   "source": [
    "# Initial work with the smaller data set\n",
    "bank = pd.read_csv('bank.csv', sep = ';')  # start with smaller data set\n",
    "\n",
    "# Examine the shape of original input data\n",
    "print(bank.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4521, 17)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
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       "      <td>no</td>\n",
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       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
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       "      <th>3</th>\n",
       "      <td>30</td>\n",
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       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome response  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown       no  \n",
       "1  cellular   11   may       220         1    339         4  failure       no  \n",
       "2  cellular   16   apr       185         1    330         1  failure       no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown       no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown       no  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop observations with missing data, if any\n",
    "bank.dropna()\n",
    "\n",
    "# Examine the shape of input data after dropping missing data\n",
    "print(bank.shape)\n",
    "\n",
    "# Examine at the list of column names, note that y is the response\n",
    "list(bank.columns.values)\n",
    "\n",
    "# Examine the beginning of the DataFrame\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predictor variables relating to client banking history:**\n",
    "- Default: Has credit in default? (yes/no) \n",
    "- Housing: Has housing loan? (yes/no)\n",
    "- Loan: Has personal loan? (yes/no)\n",
    "\n",
    "**Target variable:**\n",
    "- Response: Has the client subscribed to a term deposit? (yes/no)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
      "       'previous', 'poutcome', 'response'],\n",
      "      dtype='object')], [age           int64\n",
      "job          object\n",
      "marital      object\n",
      "education    object\n",
      "default      object\n",
      "balance       int64\n",
      "housing      object\n",
      "loan         object\n",
      "contact      object\n",
      "day           int64\n",
      "month        object\n",
      "duration      int64\n",
      "campaign      int64\n",
      "pdays         int64\n",
      "previous      int64\n",
      "poutcome     object\n",
      "response     object\n",
      "dtype: object]]\n",
      "   age          job  marital  education default  balance housing loan  \\\n",
      "0   30   unemployed  married    primary      no     1787      no   no   \n",
      "1   33     services  married  secondary      no     4789     yes  yes   \n",
      "2   35   management   single   tertiary      no     1350     yes   no   \n",
      "3   30   management  married   tertiary      no     1476     yes  yes   \n",
      "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
      "\n",
      "    contact  day month  duration  campaign  pdays  previous poutcome response  \n",
      "0  cellular   19   oct        79         1     -1         0  unknown       no  \n",
      "1  cellular   11   may       220         1    339         4  failure       no  \n",
      "2  cellular   16   apr       185         1    330         1  failure       no  \n",
      "3   unknown    3   jun       199         4     -1         0  unknown       no  \n",
      "4   unknown    5   may       226         1     -1         0  unknown       no  \n",
      "[[Index(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous',\n",
      "       'job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid',\n",
      "       'job_management', 'job_retired', 'job_self-employed', 'job_services',\n",
      "       'job_student', 'job_technician', 'job_unemployed', 'job_unknown',\n",
      "       'marital_divorced', 'marital_married', 'marital_single',\n",
      "       'education_primary', 'education_secondary', 'education_tertiary',\n",
      "       'education_unknown', 'default_no', 'default_yes', 'housing_no',\n",
      "       'housing_yes', 'loan_no', 'loan_yes', 'contact_cellular',\n",
      "       'contact_telephone', 'contact_unknown', 'month_apr', 'month_aug',\n",
      "       'month_dec', 'month_feb', 'month_jan', 'month_jul', 'month_jun',\n",
      "       'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep',\n",
      "       'poutcome_failure', 'poutcome_other', 'poutcome_success',\n",
      "       'poutcome_unknown', 'response_no', 'response_yes'],\n",
      "      dtype='object')], [age                    int64\n",
      "balance                int64\n",
      "day                    int64\n",
      "duration               int64\n",
      "campaign               int64\n",
      "pdays                  int64\n",
      "previous               int64\n",
      "job_admin.             uint8\n",
      "job_blue-collar        uint8\n",
      "job_entrepreneur       uint8\n",
      "job_housemaid          uint8\n",
      "job_management         uint8\n",
      "job_retired            uint8\n",
      "job_self-employed      uint8\n",
      "job_services           uint8\n",
      "job_student            uint8\n",
      "job_technician         uint8\n",
      "job_unemployed         uint8\n",
      "job_unknown            uint8\n",
      "marital_divorced       uint8\n",
      "marital_married        uint8\n",
      "marital_single         uint8\n",
      "education_primary      uint8\n",
      "education_secondary    uint8\n",
      "education_tertiary     uint8\n",
      "education_unknown      uint8\n",
      "default_no             uint8\n",
      "default_yes            uint8\n",
      "housing_no             uint8\n",
      "housing_yes            uint8\n",
      "loan_no                uint8\n",
      "loan_yes               uint8\n",
      "contact_cellular       uint8\n",
      "contact_telephone      uint8\n",
      "contact_unknown        uint8\n",
      "month_apr              uint8\n",
      "month_aug              uint8\n",
      "month_dec              uint8\n",
      "month_feb              uint8\n",
      "month_jan              uint8\n",
      "month_jul              uint8\n",
      "month_jun              uint8\n",
      "month_mar              uint8\n",
      "month_may              uint8\n",
      "month_nov              uint8\n",
      "month_oct              uint8\n",
      "month_sep              uint8\n",
      "poutcome_failure       uint8\n",
      "poutcome_other         uint8\n",
      "poutcome_success       uint8\n",
      "poutcome_unknown       uint8\n",
      "response_no            uint8\n",
      "response_yes           uint8\n",
      "dtype: object]]\n"
     ]
    }
   ],
   "source": [
    "# Transform data\n",
    "bank1 = pd.read_csv('bank.csv', sep = ';')\n",
    "\n",
    "print([[bank1.columns],[bank1.dtypes]])\n",
    "print(pd.DataFrame.head(bank1))\n",
    "\n",
    "bank = pd.get_dummies(bank1)\n",
    "print([[bank.columns],[bank.dtypes]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive statistics and exploratory data analysis\n",
    "- crosstabs and descriptive statistics\n",
    "- demographics of participants\n",
    "- class balance across all categorical variables, especially response variable skew\n",
    "- distributions of all features\n",
    "- pair plots and correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Housing      0     1\n",
      "Response            \n",
      "0         1661  2339\n",
      "1          301   220 \n",
      "\n",
      "Loan         0    1\n",
      "Response           \n",
      "0         3352  648\n",
      "1          478   43 \n",
      "\n",
      "Default      0   1\n",
      "Response          \n",
      "0         3933  67\n",
      "1          512   9 \n",
      "\n",
      "Odds for Response Yes for Each Variable with 95% CI\n",
      "If housing = yes, odds for response = yes: [0.519, 0.498, 0.541]\n",
      "If loan = yes, odds for response = yes: [0.465, 0.451, 0.48]\n",
      "If default = yes, odds for response = yes: [1.032, 1.002, 1.063]\n",
      "                age      balance        day    duration  campaign      pdays  \\\n",
      "response                                                                       \n",
      "no        40.998000  1403.211750  15.948750  226.347500  2.862250  36.006000   \n",
      "yes       42.491363  1571.955854  15.658349  552.742802  2.266795  68.639155   \n",
      "\n",
      "          previous  \n",
      "response            \n",
      "no        0.471250  \n",
      "yes       1.090211  \n"
     ]
    }
   ],
   "source": [
    "# Crosstabs for initial analysis\n",
    "a = pd.crosstab(bank.response_yes,\n",
    "                bank.housing_yes,\n",
    "                rownames=['Response'], colnames=['Housing'])\n",
    "                \n",
    "b = pd.crosstab(bank.response_yes,\n",
    "                bank.loan_yes,\n",
    "                rownames=['Response'], colnames=['Loan'])\n",
    "                \n",
    "c = pd.crosstab(bank.response_yes,\n",
    "                bank.default_yes,\n",
    "                rownames=['Response'], colnames=['Default'])\n",
    "\n",
    "print(a,\"\\n\")\n",
    "print(b,\"\\n\")\n",
    "print(c,\"\\n\")\n",
    "        \n",
    "def myor(a):\n",
    "        myv=round((a.loc[1,1]/a.loc[0,1])/(a.loc[1,0]/a.loc[0,0]),3)\n",
    "        logmyv = np.log(myv)\n",
    "        se = np.sqrt(1/(a.loc[1,1]+1/a.loc[0,1]+a.loc[1,0]+a.loc[0,0]))\n",
    "        lower = round(np.exp(logmyv-1.96*se),3)\n",
    "        upper = round(np.exp(logmyv+1.96*se),3)\n",
    "        return [myv, lower, upper]\n",
    "                \n",
    "print(\"Odds for Response Yes for Each Variable with 95% CI\")\n",
    "print(\"If housing = yes, odds for response = yes:\", myor(a))\n",
    "print(\"If loan = yes, odds for response = yes:\", myor(b))\n",
    "print(\"If default = yes, odds for response = yes:\", myor(c))    \n",
    "                \n",
    "print(bank1.groupby('response').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define binary variable for yes/no responses and map to original DataFrame columns\n",
    "bank1['default'] = bank1['default'].map({'yes': 1, 'no': 0})\n",
    "bank1['housing'] = bank1['housing'].map({'yes': 1, 'no': 0})\n",
    "bank1['loan'] = bank1['loan'].map({'yes': 1, 'no': 0})\n",
    "bank1['response'] = bank1['response'].map({'yes': 1, 'no': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>0</td>\n",
       "      <td>1787</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>4789</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education  default  balance  housing  loan  \\\n",
       "0   30   unemployed  married    primary        0     1787        0     0   \n",
       "1   33     services  married  secondary        0     4789        1     1   \n",
       "2   35   management   single   tertiary        0     1350        1     0   \n",
       "3   30   management  married   tertiary        0     1476        1     1   \n",
       "4   59  blue-collar  married  secondary        0        0        1     0   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome  response  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown         0  \n",
       "1  cellular   11   may       220         1    339         4  failure         0  \n",
       "2  cellular   16   apr       185         1    330         1  failure         0  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown         0  \n",
       "4   unknown    5   may       226         1     -1         0  unknown         0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.170095</td>\n",
       "      <td>0.016810</td>\n",
       "      <td>1422.657819</td>\n",
       "      <td>0.566025</td>\n",
       "      <td>0.152842</td>\n",
       "      <td>15.915284</td>\n",
       "      <td>263.961292</td>\n",
       "      <td>2.793630</td>\n",
       "      <td>39.766645</td>\n",
       "      <td>0.542579</td>\n",
       "      <td>0.115240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.576211</td>\n",
       "      <td>0.128575</td>\n",
       "      <td>3009.638142</td>\n",
       "      <td>0.495676</td>\n",
       "      <td>0.359875</td>\n",
       "      <td>8.247667</td>\n",
       "      <td>259.856633</td>\n",
       "      <td>3.109807</td>\n",
       "      <td>100.121124</td>\n",
       "      <td>1.693562</td>\n",
       "      <td>0.319347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3313.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71188.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3025.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      default       balance      housing         loan  \\\n",
       "count  4521.000000  4521.000000   4521.000000  4521.000000  4521.000000   \n",
       "mean     41.170095     0.016810   1422.657819     0.566025     0.152842   \n",
       "std      10.576211     0.128575   3009.638142     0.495676     0.359875   \n",
       "min      19.000000     0.000000  -3313.000000     0.000000     0.000000   \n",
       "25%      33.000000     0.000000     69.000000     0.000000     0.000000   \n",
       "50%      39.000000     0.000000    444.000000     1.000000     0.000000   \n",
       "75%      49.000000     0.000000   1480.000000     1.000000     0.000000   \n",
       "max      87.000000     1.000000  71188.000000     1.000000     1.000000   \n",
       "\n",
       "               day     duration     campaign        pdays     previous  \\\n",
       "count  4521.000000  4521.000000  4521.000000  4521.000000  4521.000000   \n",
       "mean     15.915284   263.961292     2.793630    39.766645     0.542579   \n",
       "std       8.247667   259.856633     3.109807   100.121124     1.693562   \n",
       "min       1.000000     4.000000     1.000000    -1.000000     0.000000   \n",
       "25%       9.000000   104.000000     1.000000    -1.000000     0.000000   \n",
       "50%      16.000000   185.000000     2.000000    -1.000000     0.000000   \n",
       "75%      21.000000   329.000000     3.000000    -1.000000     0.000000   \n",
       "max      31.000000  3025.000000    50.000000   871.000000    25.000000   \n",
       "\n",
       "          response  \n",
       "count  4521.000000  \n",
       "mean      0.115240  \n",
       "std       0.319347  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 1: Feature Distributions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distributions of features\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "bank1.hist(bins=20, figsize=(14,10), color='#E14906')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class balances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    4000\n",
      "1     521\n",
      "Name: response, dtype: int64\n",
      "------------------------\n",
      "0    0.88476\n",
      "1    0.11524\n",
      "Name: response, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - response\n",
    "# Yes = 1, No = 1\n",
    "print(bank1['response'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['response'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    4445\n",
      "1      76\n",
      "Name: default, dtype: int64\n",
      "------------------------\n",
      "0    0.98319\n",
      "1    0.01681\n",
      "Name: default, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - default\n",
    "print(bank1['default'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['default'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2559\n",
      "0    1962\n",
      "Name: housing, dtype: int64\n",
      "------------------------\n",
      "1    0.566025\n",
      "0    0.433975\n",
      "Name: housing, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - housing\n",
    "# Balance of responses suggests this may be a more meaningful feature\n",
    "print(bank1['housing'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['housing'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3830\n",
      "1     691\n",
      "Name: loan, dtype: int64\n",
      "------------------------\n",
      "0    0.847158\n",
      "1    0.152842\n",
      "Name: loan, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - loan\n",
    "print(bank1['loan'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['loan'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Participant demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34    231\n",
      "32    224\n",
      "31    199\n",
      "36    188\n",
      "33    186\n",
      "     ... \n",
      "76      2\n",
      "84      1\n",
      "81      1\n",
      "86      1\n",
      "87      1\n",
      "Name: age, Length: 67, dtype: int64\n",
      "------------------------\n",
      "34    0.051095\n",
      "32    0.049547\n",
      "31    0.044017\n",
      "36    0.041584\n",
      "33    0.041141\n",
      "        ...   \n",
      "76    0.000442\n",
      "84    0.000221\n",
      "81    0.000221\n",
      "86    0.000221\n",
      "87    0.000221\n",
      "Name: age, Length: 67, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - age\n",
    "print(bank1['age'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['age'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "married     2797\n",
      "single      1196\n",
      "divorced     528\n",
      "Name: marital, dtype: int64\n",
      "------------------------\n",
      "married     0.618668\n",
      "single      0.264543\n",
      "divorced    0.116788\n",
      "Name: marital, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - marital\n",
    "print(bank1['marital'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['marital'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "secondary    2306\n",
      "tertiary     1350\n",
      "primary       678\n",
      "unknown       187\n",
      "Name: education, dtype: int64\n",
      "------------------------\n",
      "secondary    0.510064\n",
      "tertiary     0.298607\n",
      "primary      0.149967\n",
      "unknown      0.041363\n",
      "Name: education, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - education\n",
    "print(bank1['education'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['education'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "management       969\n",
      "blue-collar      946\n",
      "technician       768\n",
      "admin.           478\n",
      "services         417\n",
      "retired          230\n",
      "self-employed    183\n",
      "entrepreneur     168\n",
      "unemployed       128\n",
      "housemaid        112\n",
      "student           84\n",
      "unknown           38\n",
      "Name: job, dtype: int64\n",
      "------------------------\n",
      "management       0.214333\n",
      "blue-collar      0.209246\n",
      "technician       0.169874\n",
      "admin.           0.105729\n",
      "services         0.092236\n",
      "retired          0.050874\n",
      "self-employed    0.040478\n",
      "entrepreneur     0.037160\n",
      "unemployed       0.028312\n",
      "housemaid        0.024773\n",
      "student          0.018580\n",
      "unknown          0.008405\n",
      "Name: job, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Value counts: total and relative - job\n",
    "print(bank1['job'].value_counts(ascending=False))\n",
    "print('------------------------')\n",
    "print(bank1['job'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>0</td>\n",
       "      <td>1787</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>4789</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>1476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education  default  balance  housing  loan  \\\n",
       "0   30   unemployed  married    primary        0     1787        0     0   \n",
       "1   33     services  married  secondary        0     4789        1     1   \n",
       "2   35   management   single   tertiary        0     1350        1     0   \n",
       "3   30   management  married   tertiary        0     1476        1     1   \n",
       "4   59  blue-collar  married  secondary        0        0        1     0   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome  response  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown         0  \n",
       "1  cellular   11   may       220         1    339         4  failure         0  \n",
       "2  cellular   16   apr       185         1    330         1  failure         0  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown         0  \n",
       "4   unknown    5   may       226         1     -1         0  unknown         0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 2: Feature Counts by Target and Predictor Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature counts by response - loan\n",
    "fig1 = sns.catplot(x=\"response\", col=\"loan\", data=bank1, kind=\"count\")\n",
    "\n",
    "# Plot feature counts by response - default\n",
    "fig1 = sns.catplot(x=\"response\", col=\"default\", data=bank1, kind=\"count\")\n",
    "\n",
    "# Plot feature counts by response - housing\n",
    "fig1 = sns.catplot(x=\"response\", col=\"housing\", data=bank1, kind=\"count\")\n",
    "\n",
    "# Plot feature counts by response - marital\n",
    "fig1 = sns.catplot(x=\"response\", col=\"marital\", data=bank1, kind=\"count\")\n",
    "\n",
    "# Plot feature counts by response - education\n",
    "fig1 = sns.catplot(x=\"response\", col=\"education\", data=bank1, kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 3: Balance Values by Predictor and Target Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot response values by balance\n",
    "sns.boxenplot(x=\"response\", y=\"balance\", data=bank1, linewidth=2.5)\n",
    "\n",
    "# Plot response values by balance and housing\n",
    "g = sns.catplot(x=\"housing\", y=\"balance\", hue=\"response\", col=\"response\", data=bank1, \n",
    "                kind=\"boxen\",height=4, aspect=.7);\n",
    "\n",
    "# Plot response values by balance and loan\n",
    "g = sns.catplot(x=\"loan\", y=\"balance\", hue=\"response\", col=\"response\", data=bank1, \n",
    "                kind=\"boxen\",height=4, aspect=.7);\n",
    "\n",
    "# Plot response values by balance and default\n",
    "g = sns.catplot(x=\"default\", y=\"balance\", hue=\"response\", col=\"response\", data=bank1, \n",
    "                kind=\"boxen\",height=4, aspect=.7);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 4: Age Values by Predictor and Target Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HIiKSYTgQEZEMw4GIiGQUv58DEfkGITmg0eoAAB0GjatyPtV9DAciqhUarQ7ffjT3pvPDnnjVg9WQu7hb6SYkSVT4l4jIlzAcbkKr1WDDrlxote7dxpSIyBsxHIiISIbhQEREMgwHIiKSYTgQEZGMouGwZ88eJCYmIjY2Fm+99RYAwGQyISEhAdHR0Vi4cKGSzRMRkYsUC4czZ85g1qxZWLp0KbZu3Ypjx45h3759SElJwdKlS7Fjxw6YzWbs27dPqRKIiMhFioXDrl27EBcXh1atWsHf3x8LFy5Ew4YN0b59e4SGhsLPzw8JCQnIzMxUqgQiInKRYldI5+fnw9/fH+PGjcPZs2fx0EMPoVOnTggJCXE+R6/Xo7CwUKkSiIjIRYqFg8PhwJEjR7Bq1So0atQI48ePR4MGDaDR/HFRmRCiwrQrzGazu6VWymAwOP+fnZ2tSBtUP1zfV2pCqb6rluqsB36X6paqPjPFwuG2226D0WhE8+bNAQAPP/wwMjMzodP9MfCWxWKBXq93q53w8HAEBga69Rq34uqXn6gqnui7dQ2/S95DsXDo27cvpk+fjosXL6Jx48bYv38/BgwYgOXLlyM/Px9t27bF9u3bkZSUpFQJLpMkCVqtFsn9u1WYJqI/VDbKak0G1+MorXWbYuEQERGB5557DiNGjIDdbkevXr3w2GOPoUOHDpg0aRKsViuioqIwYMAApUpwmVarxQdrDzmnxz1qVLEaorpJo9Uhd9k0l5fvNv6dWqyGapuiQ3YnJycjOTm5wmNGoxFbt25VslkiInIT7+dwHYckQff77qPKthaun09EVJ8xHK6j02rx5ge7bjr/tXH9PVgNEfk6W8l5BDRppkrb/DOYiKgOKjlzAl8vm4bSgpOqtM9wIEVIklTlNBHdnJAcyNvxLwDAqR3/gpAcHq+Bu5VIETzji8h1RUf3oPzSRQBAedlFFB3dg5b3ena3drW3HHJzc7FmzRrYbDZ89dVXStZEROSz7KUX8PP+TZDsNgCAZLfi5/2bYS+74NE6qrXlkJGRgRUrVsBqtaJ///6YMGECpk6dimHDhildH3kZnvFF5J5zxw/j7vEL4NegkfOx8iuX8Jv5oEe3HqoVDqtWrcLatWvxxBNPoEWLFsjIyMBzzz3HcCAZnvFF5DohSWh5X7Tscb8GjdDy3v4QkgSNh/64qlYrWq0WQUFBzunWrVtXGCOJiIhq7sYDzbf64b9xvpIHqqu15RAcHIzjx487R1DdunUrmjZtqlhRRES+QKPVwfyPFJeXDx/9di1WU1G1wiElJQUvvPACTp8+jd69eyMwMBBLly5VrCgiIlJXtcLhrrvuwpYtW5CXlweHw4E777wT/v7+StemOIdDgk5XcTOtJvvEK1ueiKg+qFY4LFmypMK0RqNBw4YN0alTJ/Tp00eRwjxBp9Ni8tzNLi+f/urgWqyGiKjuqFY4nDhxAl999RViYmKg0+mwa9cutGnTBp9++ilyc3MxceJEpeskIiIPqtY+kd9++w0ZGRlITU3Fq6++io0bN0Kj0WD16tXIzMxUukYiIvKwaoVDcXExQkJCnNPNmjVDcXExAgIC4OfHETiIiOqbav2yh4aG4t1333Ve9LZ+/Xq0a9cOOTk5vH0mEVE9VK1f9rfffhs///wzEhMTMXToUBQVFWHOnDn45ptvMH36dKVrJCIiD6vWlkN+fj7KysrQuXNnCCFw5swZDBo0CHv37lW4PCIiUkO1thxSU1Nxzz334NKlSxg4cCCaNGmC6Gj5+B83evLJJxEfH49BgwZh0KBByMnJwbZt2xAXF4fo6GisXr3a7TdARES1r1pbDhqNBmPGjMH58+fRoUMHJCQkICkpqcplhBDIy8vDZ5995jxoXVhYiKlTpyIjIwMBAQEYPnw4HnjgAXTs2NH9d0Kq4IWERPVTtcKhcePGAIB27drh5MmTMBgMtzwQ/eOPPwIAnnnmGRQXF2PYsGFo3LgxHnzwQQQHBwMAYmJikJmZieeff96d90Aq4oWERPVTtcKhW7dumDJlCl544QWMHTsWeXl5tzyF9eLFizAajXjttddgt9sxcuRIxMbGVjglVq/XIzc31603YDabXV7WYDC41TYAZGdnu/0a3ozr0PV14E7f9YSIbnfDzz+gyud0G/+OYu2X223Iyf1asdevC9T+/lTVfrUH3svJycGdd96JlJQUmEwmvPvuu1Uu06NHD/To0cM5nZycjLlz52L8+PHOx4QQzpFeXRUeHo7AwEC3XsMdtfHh+jpfXYdq993qyJ7/nGptG/76T5/tGzWh1Dqq9jGH7t27AwAeeughPPTQQ7dc5siRI7Db7TAar94NTAiBNm3awGKxOJ9jsVig1+tdKJuIiJSk2OXNJSUlSE9Px5o1a2C327Fp0ya88847mDZtGs6dO4eGDRti586dePPNN5UqgYhIVcLhgOYWN0ZT8p4M1Wn/ZhQLh759+yInJweDBw+GJEkYMWIEDAYDpk6dipEjR8JutyM5ORndunVTqgQiIlVpdDrVd825StGBkaZMmYIpU6ZUeCwhIQEJCQlKNktERG6q16PmVecceiVPpawP5/BzHRL5pnodDjqdFiP+qt5V2B/Pf1y1tmsL1yGRb+KfZEREJMNwICIiGYYDERHJMByIiEiG4UBERDIMByIikmE4EBGRDMOBiIhkGA5ERCTDcCAiIhmGAxERyTAciIhIhuFAREQyDAciIpJhOBARkQzDgYiIZBS/2c+8efNw/vx5pKWl4fjx45gxYwbKyspw77334o033oCfX72+3xAR+ajvv/8eJ06cwI8/WlSroen336Njx44uLavolsOhQ4ewadMm5/S0adMwc+ZMZGVlQQiBdevWKdk8ERG5SLE/24uLi7Fw4UKMGzcO3377LX766SdcuXIF3bt3BwAkJiYiPT0dI0aMUKoEIiLVdOzYER07dkS2OUPVGlylWDjMnDkTU6dOxdmzZwEARUVFCAkJcc4PCQlBYWGhUs0TkRu8fZcIuU+RcFi/fj1at24No9GIjIyrqSlJEjQajfM5QogK064ym803nWcwGNx+fXdlZ2erXYJbuA5vzdV1VFXfVVtBQQH8/f3VLqPOf/a3Ute/P1XVp0g47NixAxaLBYMGDcKFCxdw6dIlaDQaWCx//BXy66+/Qq/Xu91WeHg4AgMD3X4dpdSFzuHt6us6rMt999o699ZdIvQHV78/ioTDypUrnf/PyMjAF198gblz5+KRRx5BdnY2DAYDtmzZgsjISCWaB/DHZvG5n75RrI3q1ODNHZzrkMh3efQ80gULFiA1NRWlpaXo2rUrRo4c6cnmiYiomhQPh8TERCQmJgIAwsLCsGHDBqWbBPDHmQIf7T3vkfZuVoM34zok8l28QpqIiGQYDkREJMNwICIiGYYDERHJMByIiEiG4UBERDIcL5uISCHC4YDhr/9UtX2NTufSsgwHIiKFVOeH2fyPFJdfP3z02263fzPcrURERDIMByIikmE4EBGRDMOBiIhkGA5ERCTDcCAiIpl6fSqrwyHh4/mPq9q+Tsf8JSLvU6/DoTo/zJPnbnb59dNfHex2+0REdRF/vYiISKZebzkQkeu8eegHbyEkxy2vcr7V8hqtMuuI4UBElarOD3Pusmkuv3638e+43b63q+yH/eSGRTd9fqfkF265fG1RdLfSokWLEBcXh/j4eKxcuRIAYDKZkJCQgOjoaCxcuFDJ5omIyEWKbTl88cUX+Pzzz7F161aUl5cjLi4ORqMRKSkpWLVqFVq3bo2xY8di3759iIqKUqoMchPP+CLyHCFJsq2DG+drtJ75PigWDvfffz8+/PBD+Pn5obCwEA6HAxcvXkT79u0RGhoKAEhISEBmZibDoQ7jGV9EnnPth18qt0HrF+B8/Nq0p4IBUHi3kr+/P9LT0xEfHw+j0YiioiKEhIQ45+v1ehQWFipZAhGRV7GXXkDO4ikov3IJwNWthZzFU2Evu+DROhQ/ID158mSMHj0a48aNQ15eHjQajXOeEKLCtCvMZrPLy97drdst/3Ktis1ux9e5uS4vXx8YDAa3XyM7O7sWKlGPq+vAnb5bF/CzV8jpHMDhQE765D8e0+qQu3Mj0C6iVpuq6jNULBx++OEH2Gw2dO7cGQ0bNkR0dDQyMzOhu+4MBIvFAr1e71Y74eHhCAwMdLdcpzc/2HXTea+N619hOsDfv1a+IL7OV9dhbfddb+Srn31V7GEdYV5+BJLkcD6m1fkhPDoJ/o2beqwOxcKhoKAA6enp+M9//gMA2L17N4YPH4758+cjPz8fbdu2xfbt25GUlKRUCUSkICE5bnk66q2WV/JUTG/l37gpbu8zBD/v3wTJboPWPwC39xns0WAAFAyHqKgo5ObmYvDgwdDpdIiOjkZ8fDyaN2+OSZMmwWq1IioqCgMGDFCqBCJSUGU/7N9+NPemzw974tVbLk9X6e/ph6Kje2ArtsCvcVPo7+nn8RoUPeYwadIkTJo0qcJjRqMRW7duVbJZIiKvptHqcEfs0zjxn/m4M+4ZVYKUV0hfxyFJsuMKN87XefBUMiLyXU1C/4S7x7+DgCbNVGmf4XCd63/4P1h7yPn/cY8aZfOJiJSmVjAAHJWViIgqwS0HcovDIbl1rQiHx6g/hOSQHXS+cT4PQnsPhgO5pbIf9ppcK8JgqD+u/+H/ccsHzv93GDRONp/qPn4ziYhIhlsOlZAkyXkQ+tq0lgejq4VnfBHVDwyHSlwLgg27cpHcvxuDoQZ4xhdR/aARQgi1i3CF1WqF2WxWbHwaSRLQajXOf6lmbtza4tbXH5Tuu2q78Z4DnrwHAdUefmI3cS0QGAyuuX7r6/ppqv+uBcGZPWsrTJN34adGREQyPOZAinA4JFjtDiT37wZJEhBC8LRVIi/CbyspIuvgd5jw1kYAV3fN7TSdULkiIqoJhgPVuuKSy1i3MxdWmwOSELh02Ya1WTkoLrmsdmlEVE0Mh5uQhIDknSdyqc70VR4kSQIAfPtjEZ6dtR6SJMH0v/kqV0aeIiQJof0ehfi9H5D3YTjchFajgdbN+1v7qp497nCenfTm3/8L4OrZSj27t1ezLPKga2co8Uwl78VPjmpdcJOGGBbdDYEBV8fSCQzQ4dGYCAQ3aahyZURUXQwHUkRMrz+jadDVMAhu0hDRPf+kckXkSUJIEIK7lLwZw+Emsg5+BwDYafpO5Uq8k06nxfjfh8wYN8zI01h9jEajhUbDz9ybKfrpLVmyBPHx8YiPj8f8+fMBACaTCQkJCYiOjsbChQuVbN5lNls5PtyWDQD4f1uzYbOVq1yRdwq7U4+/zRiCsDv1apdCRDWkWDiYTCYcOHAAmzZtwubNm/HNN99g+/btSElJwdKlS7Fjxw6YzWbs27dPqRJclv7xAUiSwIZduZAkgcUfH1S7JK/VvGkjtUsgIhcoFg4hISF45ZVXEBAQAH9/f9x1113Iy8tD+/btERoaCj8/PyQkJCAzM1OpElySf/Y8so/9BADYuOtrAMCRYwU4ffa8mmUREXmUYsNndOrUyfn/vLw8fPrpp3jiiScQEhLifFyv16OwsNCtdsxms1vL32jT/oJKH//H2v+PwX3a1mpbVD8YDAaXlqvtvlvXGAwGZGdnq10GVaGqvqv42EonT57E2LFj8de//hU6nQ55eXnOeUIIaNy8lqC2hz0Oub0Dpi/cIXt89KORaNe6Wa21Q1Rfh+y+nqvBSepT9IB0dnY2Ro0ahZdeeglDhgxBq1atYLFYnPMtFgv0+rp1sJLXRBMRKRgOZ8+excSJE7FgwQLEx8cDACIiInDq1Cnk5+fD4XBg+/btiIyMVKoEl6zPyqn88Z25Hq6EiEg9iu1WWrFiBaxWK9LS0pyPDR8+HGlpaZg0aRKsViuioqIwYMAApUpwybCYCOcB6esNje6mQjVEROrgbUIrseDfeysExL1d2uKlUVG12gb5rvp+m1CqH3gJYyUmj+jtvD2oTqvBpBG9VK6IiMizGA6VCAjww8iEq2dZjBxoQEAAb5hHRL6Fv3o3EdPrz+jU7jZ0CG2hdilERB7HLYcqMBiIyFcxHIiISIbhQEREMl57zOHaGbg2m03lSoiAgICAag8Fw5TjGyYAAASWSURBVL5LdcnN+q7XhoPdbgcAnDhxQuVKiGo2ThL7LtUlN+u7XnsRnCRJKCsrg7+/v9uD9xG5qyZbDuy7VJfcrO96bTgQEZFyeECaiIhkGA5ERCTDcCAiIhmGAxERyTAciIhIhuFAREQyDAciIpJhOBARkQzDgYiIZBgOREQkw3BQyLZt2xAXF4fo6GisXr1a7XK8VmlpKR555BEUFBSoXYrPYN+tHd7edxkOCigsLMTChQvx8ccfY/PmzVi7di2+//57tcvyOjk5OXjssceQl5endik+g323dtSHvstwUIDJZMKDDz6I4OBgNGrUCDExMcjMzFS7LK+zbt06zJo1C3q9Xu1SfAb7bu2oD33Xa+/nUJcVFRUhJCTEOa3X65Gbm6tiRd5pzpw5apfgc9h3a0d96LvcclCAJEkVxkcXQnDcfvIK7Lt0DcNBAa1atYLFYnFOWywWr968JN/BvkvXMBwU0LNnTxw6dAjnzp3D5cuXsXPnTkRGRqpdFtEtse/SNTzmoICWLVti6tSpGDlyJOx2O5KTk9GtWze1yyK6JfZduoa3CSUiIhnuViIiIhmGAxERyTAciIhIhuFAREQyDAciIpJhONQTmZmZePLJJ6t8zoEDB9C3b18kJyfjypUrNW6joKAAPXr0AACcOXMGkyZNcqlWohux/9Y9DAcf8sknn2Do0KHYsGEDGjRo4NZr/fzzzzh16lQtVUZ0a+y/nsWL4LzYokWLsG3bNgQHB6N9+/YAAJvNhgULFuDLL7+Ew+FAly5dkJqaijVr1mD37t0IDAxESUkJnn32WcycORO//fYbLBYL2rRpg/fffx8tWrRAv379sGjRItx9990A4Jxu1qwZAMDhcCA1NRWFhYV49tlnsWLFCtXWAXkv9t+6jVsOXuq///0vdu7cic2bN2PNmjUoLS0FACxfvhw6nQ4ZGRnYunUr9Ho9FixYgOeeew79+vXDqFGjMH36dHzyySfo3r071q5di927d6NBgwbYsmVLtdrW6XR466230K5dO36xyCXsv3Uftxy81KFDh9C/f38EBQUBAJKSkrBq1Srs3bsXJSUlMJlMAAC73Y4WLVrIln/qqadw5MgRrFy5Enl5eTh58iQiIiI8+h7Id7H/1n0MBy92/cgnOp0OwNUhl1NSUhAVFQUAKCsrg9VqlS37zjvvIDc3F0lJSXjggQdQXl5e4fWu/7/NZlPqLZAPY/+t27hbyUtFRkYiMzMTFy9ehCRJzk3q3r17Y/Xq1bDZbJAkCa+99hree+892fIHDhzAU089hcGDB6NFixYwmUxwOBwAgObNm8NsNgMADh8+XGEI52t0Oh3sdruC75DqM/bfuo/h4KWioqKQlJSEpKQkDB06FE2aNAEATJgwAW3atMGQIUMQFxcHIQReeeUV2fITJ07E/PnzkZCQgPHjx+Oee+7B6dOnAQAvv/wyPvzwQwwaNAhbtmxB165dZct37NgRgYGBSE5OBsdupJpi/637OCorERHJcMuBiIhkGA5ERCTDcCAiIhmGAxERyTAciIhIhuFAREQyDAciIpJhOBARkcz/Abp7qB2pcPPuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 403.2x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot response values by balance\n",
    "sns.boxenplot(x=\"response\", y=\"age\", data=bank1, linewidth=2.5)\n",
    "\n",
    "# Plot response values by balance and housing\n",
    "g = sns.catplot(x=\"housing\", y=\"age\", hue=\"response\", col=\"response\", data=bank1, \n",
    "                kind=\"boxen\",height=4, aspect=.7);\n",
    "\n",
    "# Plot response values by balance and loan\n",
    "g = sns.catplot(x=\"loan\", y=\"age\", hue=\"response\", col=\"response\", data=bank1, \n",
    "                kind=\"boxen\",height=4, aspect=.7);\n",
    "\n",
    "# Plot response values by balance and default\n",
    "g = sns.catplot(x=\"default\", y=\"age\", hue=\"response\", col=\"response\", data=bank1, \n",
    "                kind=\"boxen\",height=4, aspect=.7);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 5: Balance Values by Target and Non-Predictor Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.55x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 914.55x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Balance by Marital Status, Age, and Response\n",
    "sns.set(style=\"whitegrid\")\n",
    "g = sns.FacetGrid(bank1, hue=\"response\", col=\"marital\", margin_titles=True,\n",
    "                  palette={1:\"seagreen\", 0:\"darkorange\"})\n",
    "g = g.map(plt.scatter, \"balance\", \"age\",edgecolor=\"w\").add_legend();\n",
    "\n",
    "# Plot Balance by Education, Age, and Response\n",
    "g = sns.FacetGrid(bank1, hue=\"response\", col=\"education\", margin_titles=True,\n",
    "                  palette={1:\"seagreen\", 0:\"darkorange\"})\n",
    "g = g.map(plt.scatter, \"balance\", \"age\",edgecolor=\"w\").add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 6: Age by Education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c66250e80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot response values by balance and loan\n",
    "ax = sns.boxenplot(x=bank1[\"age\"])\n",
    "\n",
    "# Median age by education\n",
    "sns.boxenplot(x=\"age\", y=\"education\", data=bank1, linewidth=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 7: Feature Pair Plots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1306.5x1260 with 56 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair plots of selected features color-coded by response values\n",
    "g = sns.pairplot(bank1, vars=[\"age\", \"marital\", \"education\", \"default\", \"balance\", \"housing\", \"loan\"], \n",
    "                 hue=\"response\", palette=\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8: Correlation Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116167cc0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix of all features\n",
    "plt.figure(figsize=(15,10))\n",
    "corr = bank1.corr(method='pearson')\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr, mask=mask, annot=True, vmax=.3, cmap=\"RdYlBu\", square=True, linewidths = .5, \n",
    "            cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data split strategy 1 - stratified train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   default  housing  loan  response\n",
       "0        0        0     0         0\n",
       "1        0        1     1         0\n",
       "2        0        1     0         0\n",
       "3        0        1     1         0\n",
       "4        0        1     0         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select subset of features\n",
    "# Yes = 1, N0 = 0\n",
    "bank_model_df = bank1.loc[:, ['default','housing','loan','response']]\n",
    "bank_model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (4521, 3)\n",
      "y shape:  (4521,)\n",
      "X_train.shape:  (3390, 3)\n",
      "X_test.shape:  (1131, 3)\n",
      "y_train.shape:  (3390,)\n",
      "y_test.shape:  (1131,)\n"
     ]
    }
   ],
   "source": [
    "# Prepare data for train/test splitting\n",
    "X = bank_model_df.iloc[:,0:3].values\n",
    "y = bank_model_df.iloc[:,3].values\n",
    "\n",
    "print('X shape: ', X.shape)\n",
    "print('y shape: ', y.shape)\n",
    "\n",
    "# Split into train and test sets and review\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n",
    "\n",
    "print('X_train.shape: ', X_train.shape)\n",
    "print('X_test.shape: ', X_test.shape)\n",
    "print('y_train.shape: ', y_train.shape)\n",
    "print('y_test.shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data split strategy 2 - upsampling\n",
    "This intends to deal with the imbalance of Yes reponses (521) to No responses (4000) in the original data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11523999115239991\n"
     ]
    }
   ],
   "source": [
    "# Training set\n",
    "X = pd.DataFrame(bank[['loan_yes', 'default_yes', 'housing_yes', 'response_yes']])\n",
    "trainnum = random.sample(range(1,4521), 521)\n",
    "train = X.loc[trainnum]\n",
    "test = X.drop(X.index[trainnum])\n",
    "\n",
    "X_train = train[['loan_yes', 'default_yes', 'housing_yes']]\n",
    "y_train = val.column_or_1d(train[['response_yes']])\n",
    "\n",
    "# Display proportion of Response = yes samples\n",
    "print(np.mean(X['response_yes']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test set\n",
    "X_test = np.array(test[['loan_yes', 'default_yes', 'housing_yes']])\n",
    "y_test = np.array(val.column_or_1d(test[['response_yes']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    464\n",
       "0    464\n",
       "Name: response_yes, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upsampling to address imbalance of \"no\" responses\n",
    "\n",
    "# Separate majority and minority classes\n",
    "minority = train[train['response_yes']==1]\n",
    "majority = train[train['response_yes']==0]\n",
    "\n",
    "# Upsample minority class and create new DataFrame\n",
    "upsampled = resample(minority, replace=True, n_samples=len(majority), random_state=123)\n",
    "newbank = pd.concat([majority, upsampled])\n",
    "newbank.response_yes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>loan_yes</th>\n",
       "      <th>default_yes</th>\n",
       "      <th>housing_yes</th>\n",
       "      <th>response_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4443</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      loan_yes  default_yes  housing_yes  response_yes\n",
       "4443         0            0            0             0\n",
       "1305         1            0            1             0\n",
       "1922         1            0            1             0\n",
       "2167         0            0            0             0\n",
       "1456         1            0            1             0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newbank.head()\n",
    "# len(newbank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (928, 3)\n",
      "y shape:  (928,)\n",
      "X_train.shape:  (696, 3)\n",
      "X_test.shape:  (232, 3)\n",
      "y_train.shape:  (696,)\n",
      "y_test.shape:  (232,)\n"
     ]
    }
   ],
   "source": [
    "# Prepare upsampled data for train/test splitting\n",
    "X = newbank.drop('response_yes', axis=1)\n",
    "y = newbank.response_yes\n",
    "\n",
    "print('X shape: ', X.shape)\n",
    "print('y shape: ', y.shape)\n",
    "\n",
    "# Split into train and test sets and review\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n",
    "\n",
    "print('X_train.shape: ', X_train.shape)\n",
    "print('X_test.shape: ', X_test.shape)\n",
    "print('y_train.shape: ', y_train.shape)\n",
    "print('y_test.shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning phase \n",
    "Employ cross-validation design to test out accuracy for multiple classification models on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Cross-validation\n",
    "nfolds = 10\n",
    "\n",
    "# Logistic Regression\n",
    "log_reg = LogisticRegression(solver=\"lbfgs\")\n",
    "log_scores = cross_val_score(log_reg, X_train, y_train, cv=nfolds)\n",
    "log_reg_mean = log_scores.mean()\n",
    "\n",
    "# Naives Bayes - Gaussian\n",
    "nav_clf = GaussianNB()\n",
    "nav_scores = cross_val_score(nav_clf, X_train, y_train, cv=nfolds)\n",
    "nav_mean = nav_scores.mean()\n",
    "\n",
    "# Naive Bayes - Bernoulli\n",
    "bern_clf = BernoulliNB()\n",
    "bern_scores = cross_val_score(bern_clf, X_train, y_train, cv=nfolds)\n",
    "bern_mean = bern_scores.mean()\n",
    "\n",
    "# SVC\n",
    "svc_clf = SVC()\n",
    "svc_scores = cross_val_score(svc_clf, X_train, y_train, cv=nfolds)\n",
    "svc_mean = svc_scores.mean()\n",
    "\n",
    "# KNearestNeighbors\n",
    "knn_clf = KNeighborsClassifier(weights=\"distance\")\n",
    "knn_scores = cross_val_score(knn_clf, X_train, y_train, cv=nfolds)\n",
    "knn_mean = knn_scores.mean()\n",
    "\n",
    "# Decision Tree\n",
    "tree_clf = tree.DecisionTreeClassifier()\n",
    "tree_scores = cross_val_score(tree_clf, X_train, y_train, cv=nfolds)\n",
    "tree_mean = tree_scores.mean()\n",
    "\n",
    "# Gradient Boosting Classifier\n",
    "grad_clf = GradientBoostingClassifier()\n",
    "grad_scores = cross_val_score(grad_clf, X_train, y_train, cv=nfolds)\n",
    "grad_mean = grad_scores.mean()\n",
    "\n",
    "# Random Forest Classifier\n",
    "rand_clf = RandomForestClassifier(n_estimators=18)\n",
    "rand_scores = cross_val_score(rand_clf, X_train, y_train, cv=nfolds)\n",
    "rand_mean = rand_scores.mean()\n",
    "\n",
    "# NeuralNet Classifier\n",
    "neural_clf = MLPClassifier(alpha=1)\n",
    "neural_scores = cross_val_score(neural_clf, X_train, y_train, cv=nfolds)\n",
    "neural_mean = neural_scores.mean()\n",
    "\n",
    "# Stochastic Gradient Descent\n",
    "sgd_clf = SGDClassifier(loss='log')\n",
    "sgd_scores = cross_val_score(sgd_clf, X_train, y_train, cv=nfolds)\n",
    "sgd_mean = sgd_scores.mean()\n",
    "\n",
    "# Create a Dataframe with the results.\n",
    "d = {'Classifiers': ['Logistic Reg.', 'Naives Bayes Gaussian', 'Naive Bayes Bernoulli', 'SVC', 'KNN', 'Dec Tree', 'Grad B CLF', 'Rand FC', 'Neural Classifier', 'SGD'], \n",
    "    'Cross-validated Mean Scores': [log_reg_mean, svc_mean, knn_mean, tree_mean, grad_mean, rand_mean, neural_mean, nav_mean, bern_mean, sgd_mean]}\n",
    "\n",
    "result_df = pd.DataFrame(data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Classifiers</th>\n",
       "      <th>Cross-validated Mean Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naives Bayes Gaussian</td>\n",
       "      <td>0.642091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.642091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.642091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dec Tree</td>\n",
       "      <td>0.642091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Reg.</td>\n",
       "      <td>0.620663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Grad B CLF</td>\n",
       "      <td>0.620663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Neural Classifier</td>\n",
       "      <td>0.620663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rand FC</td>\n",
       "      <td>0.612050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naive Bayes Bernoulli</td>\n",
       "      <td>0.555901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SGD</td>\n",
       "      <td>0.521781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Classifiers  Cross-validated Mean Scores\n",
       "1  Naives Bayes Gaussian                     0.642091\n",
       "3                    SVC                     0.642091\n",
       "4                    KNN                     0.642091\n",
       "5               Dec Tree                     0.642091\n",
       "0          Logistic Reg.                     0.620663\n",
       "6             Grad B CLF                     0.620663\n",
       "8      Neural Classifier                     0.620663\n",
       "7                Rand FC                     0.612050\n",
       "2  Naive Bayes Bernoulli                     0.555901\n",
       "9                    SGD                     0.521781"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results DataFrame\n",
    "result_df = result_df.sort_values(by=['Cross-validated Mean Scores'], ascending=False)\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 9: Cross-Validated F1 Scores for Logistic Regression and Naive Bayes Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c6a7247f0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use YellowBrick Cross Val visualization for alternate evaluation\n",
    "\n",
    "# Load the classification dataset\n",
    "X, y = X_train, y_train\n",
    "\n",
    "# Create a cross-validation strategy\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "\n",
    "# Instantiate the classification model and visualizer - LR\n",
    "model = LogisticRegression(solver=\"lbfgs\")\n",
    "visualizer = CVScores(model, cv=cv, scoring='f1_weighted')\n",
    "\n",
    "visualizer.fit(X, y)        # Fit the data to the visualizer\n",
    "visualizer.show()           # Finalize and render the figure\n",
    "\n",
    "# Instantiate the classification model and visualizer - NB\n",
    "model = BernoulliNB()\n",
    "visualizer = CVScores(model, cv=cv, scoring='f1_weighted')\n",
    "\n",
    "visualizer.fit(X, y)        # Fit the data to the visualizer\n",
    "visualizer.show()           # Finalize and render the figure\n",
    "\n",
    "# Instantiate the classification model and visualizer - SVC\n",
    "model = SVC()\n",
    "visualizer = CVScores(model, cv=cv, scoring='f1_weighted')\n",
    "\n",
    "visualizer.fit(X, y)        # Fit the data to the visualizer\n",
    "visualizer.show()           # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 10: Scatterplot - Logistic Regression and Naive Bayes (Bernoulli) Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c6a87ce10>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot of two models - LogReg and Naive Bayes (Bernoulli)\n",
    "# Two models produce same results as training set size approaches infinity (?)\n",
    "# NB assumption holds that xi's are conditionally independent of each other given y\n",
    "plt.scatter(log_scores, bern_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 11: Scatterplot - Logistic Regression and SVC Models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c6a961160>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot of two models - LogReg and SVC\n",
    "# Two models produce same results as training set size approaches infinity (?)\n",
    "# NB assumption holds that xi's are conditionally independent of each other given y\n",
    "plt.scatter(log_scores, svc_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid search to fine-tune models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search to fine-tune short list of models\n",
    "# Try different C and alpha values\n",
    "# Try L1 versus L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter grid:\n",
      "{'C': [0.001, 0.01, 0.1, 1, 5, 10]}\n",
      "Parameter grid:\n",
      "{'alpha': [0.001, 0.01, 0.1, 1, 5, 10]}\n",
      "Parameter grid:\n",
      "{'C': [0.001, 0.01, 0.1, 1, 5, 10]}\n",
      "Test set score: 0.61\n",
      "Best parameters: {'C': 1}\n",
      "Best cross-validation score: 0.62\n",
      "Best estimator:\n",
      "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# Classifier parameters\n",
    "lr_param = {'C': [0.001, 0.01, 0.1, 1, 5, 10]}\n",
    "nb_param = {'alpha': [0.001, 0.01, 0.1, 1, 5, 10]}\n",
    "svc_param = {'C': [0.001, 0.01, 0.1, 1, 5, 10]}\n",
    "\n",
    "print(\"Parameter grid:\\n{}\".format(lr_param))\n",
    "print(\"Parameter grid:\\n{}\".format(nb_param))\n",
    "print(\"Parameter grid:\\n{}\".format(svc_param))\n",
    "\n",
    "# LR\n",
    "gs_lr = GridSearchCV(LogisticRegression(), lr_param, cv=10, scoring=\"roc_auc\", return_train_score=True)\n",
    "gs_lr.fit(X_train, y_train)\n",
    "print(\"Test set score: {:.2f}\".format(gs_lr.score(X_test, y_test)))\n",
    "print(\"Best parameters: {}\".format(gs_lr.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(gs_lr.best_score_))\n",
    "print(\"Best estimator:\\n{}\".format(gs_lr.best_estimator_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.66\n",
      "Best parameters: {'alpha': 0.001}\n",
      "Best cross-validation score: 0.63\n",
      "Best estimator:\n",
      "BernoulliNB(alpha=0.001, binarize=0.5, class_prior=[0.5, 0.5], fit_prior=False)\n"
     ]
    }
   ],
   "source": [
    "# NB\n",
    "gs_nb = GridSearchCV(BernoulliNB(binarize=0.5, class_prior = [0.5, 0.5], fit_prior=False), nb_param, cv=10, \n",
    "                    scoring=\"roc_auc\", return_train_score=True)\n",
    "gs_nb.fit(X_train, y_train)\n",
    "print(\"Test set score: {:.2f}\".format(gs_nb.score(X_test, y_test)))\n",
    "print(\"Best parameters: {}\".format(gs_nb.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(gs_nb.best_score_))\n",
    "print(\"Best estimator:\\n{}\".format(gs_nb.best_estimator_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.58\n",
      "Best parameters: {'C': 5}\n",
      "Best cross-validation score: 0.65\n",
      "Best estimator:\n",
      "SVC(C=5, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "# SVC\n",
    "gs_svc = GridSearchCV(SVC(), svc_param, cv=10, scoring=\"roc_auc\", return_train_score=True)\n",
    "gs_svc.fit(X_train, y_train)\n",
    "print(\"Test set score: {:.2f}\".format(gs_svc.score(X_test, y_test)))\n",
    "print(\"Best parameters: {}\".format(gs_svc.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(gs_svc.best_score_))\n",
    "print(\"Best estimator:\\n{}\".format(gs_svc.best_estimator_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Revise Logistic Regression and Naive Bayes parameters\n",
    "log_reg = LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
    "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
    "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
    "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
    "                   warm_start=False)\n",
    "\n",
    "bern_clf = BernoulliNB(alpha=0.001, binarize=0.5, class_prior=[0.5, 0.5], fit_prior=False)\n",
    "\n",
    "svc_clf = SVC(C=5, probability=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit training set\n",
    "- log_reg\n",
    "- bern_clf\n",
    "- svc_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit Logistic Regression model\n",
    "log_reg.fit(X_train, y_train)\n",
    "lr_pred = log_reg.predict_proba(X_train)\n",
    "lr_pred = [p[1] for p in lr_pred]\n",
    "lr_pred_class = log_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit Naive Bayes model\n",
    "bern_clf.fit(X_train, y_train)\n",
    "bern_pred = bern_clf.predict_proba(X_train)\n",
    "bern_pred = [p[1] for p in bern_pred]\n",
    "bern_pred_class = bern_clf.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit SVC model\n",
    "svc_clf.fit(X_train, y_train)\n",
    "svc_pred = svc_clf.predict_proba(X_train)\n",
    "svc_pred = [p[1] for p in svc_pred]\n",
    "svc_pred_class = svc_clf.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: \n",
      "\n",
      "Area Under the Curve:  0.6244054696789537\n",
      "\n",
      " Confusion Matrix: \n",
      " [[203 145]\n",
      " [119 229]]\n",
      "\n",
      " PPV, Correctly Classifies Response Yes: \n",
      " 0.6580459770114943\n",
      "\n",
      " NPV, Correctly Classifies Response No: \n",
      " 0.5833333333333334\n",
      "-------------------\n",
      "Naive Bayes (Bernoulli): \n",
      "\n",
      "Area Under the Curve:  0.6134974897608667\n",
      "\n",
      " Confusion Matrix: \n",
      " [[203 145]\n",
      " [119 229]]\n",
      "\n",
      " PPV, Correctly Classifies Response Yes: \n",
      " 0.6580459770114943\n",
      "\n",
      " NPV, Correctly Classifies Response No: \n",
      " 0.5833333333333334\n",
      "-------------------\n",
      "SVC: \n",
      "\n",
      "Area Under the Curve:  0.6366015986259743\n",
      "\n",
      " Confusion Matrix: \n",
      " [[190 158]\n",
      " [ 91 257]]\n",
      "\n",
      " PPV, Correctly Classifies Response Yes: \n",
      " 0.7385057471264368\n",
      "\n",
      " NPV, Correctly Classifies Response No: \n",
      " 0.5459770114942529\n",
      "-------------------\n"
     ]
    }
   ],
   "source": [
    "# Define function for model performance metrics\n",
    "def metrics(y, pred, predclass):\n",
    "    print(\"Area Under the Curve: \", roc_auc_score(y, pred))\n",
    "    myconfusion = confusion_matrix(y, predclass)\n",
    "    PPV = (myconfusion[1,1]/(myconfusion[1,0]+myconfusion[1,1]))\n",
    "    NPV = (myconfusion[0,0]/(myconfusion[0,0]+myconfusion[0,1]))\n",
    "    print(\"\\n Confusion Matrix: \\n\", myconfusion)\n",
    "    print(\"\\n PPV, Correctly Classifies Response Yes: \\n\", PPV)\n",
    "    print(\"\\n NPV, Correctly Classifies Response No: \\n\", NPV)\n",
    "    \n",
    "print(\"Logistic Regression: \\n\")\n",
    "metrics(y_train, lr_pred, lr_pred_class)\n",
    "print('-------------------')\n",
    "print(\"Naive Bayes (Bernoulli): \\n\")\n",
    "metrics(y_train, bern_pred, bern_pred_class)\n",
    "print('-------------------')\n",
    "print(\"SVC: \\n\")\n",
    "metrics(y_train, svc_pred, svc_pred_class)\n",
    "print('-------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  [0.50144092 0.60090703 0.60364465 0.61229947 0.60821918 0.33333333\n",
      " 0.34782609 1.        ]\n",
      "------------------------\n",
      "Recall:  [1.         0.76149425 0.76149425 0.65804598 0.63793103 0.02298851\n",
      " 0.02298851 0.        ]\n",
      "------------------------\n",
      "Thresholds:  [0.46056888 0.46080384 0.46124149 0.53336308 0.53380142 0.53403676\n",
      " 0.53447502]\n"
     ]
    }
   ],
   "source": [
    "# Precision recall curve - Logistic Regression\n",
    "precision, recall, thresholds = precision_recall_curve(y_train, lr_pred)\n",
    "print('Precision: ',precision)\n",
    "print('------------------------')\n",
    "print('Recall: ', recall)\n",
    "print('------------------------')\n",
    "print('Thresholds: ', thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  [0.5        0.49602544 0.60904255 0.61229947 0.63142857 0.62756598\n",
      " 1.        ]\n",
      "------------------------\n",
      "Recall:  [1.         0.89655172 0.65804598 0.65804598 0.63505747 0.61494253\n",
      " 0.        ]\n",
      "------------------------\n",
      "Thresholds:  [0.35433557 0.37187963 0.37187963 0.59653044 0.61465051 0.61465051]\n"
     ]
    }
   ],
   "source": [
    "# Precision recall curve - Naive Bayes\n",
    "precision, recall, thresholds = precision_recall_curve(y_train, bern_pred)\n",
    "print('Precision: ',precision)\n",
    "print('------------------------')\n",
    "print('Recall: ', recall)\n",
    "print('------------------------')\n",
    "print('Thresholds: ', thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  [0.5        0.59819413 0.59954751 0.61336516 0.61630695 0.61927711\n",
      " 0.58108108 0.77777778 1.        ]\n",
      "------------------------\n",
      "Recall:  [1.         0.76149425 0.76149425 0.73850575 0.73850575 0.73850575\n",
      " 0.12356322 0.02011494 0.        ]\n",
      "------------------------\n",
      "Thresholds:  [0.34273207 0.34273207 0.34275808 0.34279046 0.34280553 0.61694899\n",
      " 0.61704054 0.61706307]\n"
     ]
    }
   ],
   "source": [
    "# Precision recall curve - SVC\n",
    "precision, recall, thresholds = precision_recall_curve(y_train, svc_pred)\n",
    "print('Precision: ',precision)\n",
    "print('------------------------')\n",
    "print('Recall: ', recall)\n",
    "print('------------------------')\n",
    "print('Thresholds: ', thresholds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 12: Logistic Regression Precision-Recall Curve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jjzyCXr16AQD8/PwwdOhQLF26FD///DOaNWuG27dvQ6PRmO1LcnJyrfbNHjnKftYW26dqbJ+qsX3uX7VJ2mAwQHT35RqAsddXdrqwsBBz587Fhg0bEB4ejk8//RSzZ8/GmjVrah1UZYNfV6bsQOL2NrC4m5sboqKiMGfOHADApUuX8O6776Jnz5547733IJfLceavG8hLykL70BD06dPHbP22bdsiNTUVUVFRSEpKwmOPPYZu3bqZlpdti27duiE+Ph7t2rXDlStXkJSUhDNnzpi2ExoaCm9vb9y6dctsvaCgILtq09qyt2PH0tg+VWP7VI3tU7GioqIqO6bVJml/f38cPXrUNK1Wq+Hr62uavnjxIhQKBcLDwwEA//jHP7B8+fK6xOxw3NzcEBYWBgAICwtDQEAA/vWvf0EulxsTtUQGXYEWp46cKNfjBmA605GTkwNvb+9K61Gr1Xjvvfdw8uRJyGQytGvXDk2bNgVg/PJFRES2pdok3atXL6xYsQKZmZlwcnLC7t278dZbb5mWt2jRAqmpqbhy5Qpat26N3377zZRwqHZ69uyJESNG4H//+x8GDhwIuasMEqUUnbqG4c2XXy9X3sXFBQDg6upa7kay7OxsnDlzBl26dMGMGTOQmpqKTZs2oVOnTpBKpdi7dy92795tkf0iIqL7U+3d3X5+foiLi8O4ceMwbNgwDBkyBOHh4Zg4cSJOnz4Nd3d3LFy4EC+99BJiY2Px/fffY8GCBZaIvVGbPn063NzcsGjRIkgEEVyauyP1xi0EBwcjLCwMYWFhaN++PT766CPTdZ7IyEj8+eefZqNn/fzzz3j++eeh1+tx4sQJ9OzZExEREZBKjd/P9u3bB4A9aSIiW1Sj56RjY2MRGxtrNm/t2rWmz3379kXfvn3rNzIH5+Xlheeffx6LFy/Gyd3xUPVqjqsJJzBhwgSMGzcOMpkMn3zyCU6cOIGXXnoJAPDCCy9g9OjRmDZtGp5++mmkpqZi2bJlGDNmDFxdXREWFoY///wTO3fuhLu7O3755Rd8/fXXAIz3FhARkW3hu7tt2Pjx49G0aVP8vfl3iGViTHpnCpycnDBz5kzExcXBYDDg008/RYcOHQAAnTt3xvr166FWq/Hiiy9i9erVGDt2LGbMmAEAWLhwIQIDAzFnzhzExcUhMTERX3zxBZydnXHixAlr7ioREVWArwW1si+++KLSZXK5HHv27MHmszvwzemt8Aj0xn//+98qt9ejRw9s2rSpwmXNmzfHnDlzyt1hefz4cdPnRYsWmS27cOFCdbtAREQNhD1pO6CQGF8yUqwrtnIkRERkSUzSdkBekqT1WitHQkRElsQkbQfkEhkAJmkiIkfDJG0H5FJjki7S83Q3EZEjYZK2AzzdTUTkmJik7UDp6W72pImIHAmTtB3gNWkiIsfEJG0HeLqbiMgxMUnbAZ7uJiJyTEzSdoCnu4mIHBOTtB1QSBUAgNzCOziddt7K0RARkaUwSdsBN7kLujaNgNagw9t/fIivTm6BzqC3dlhERNTAmKTtgEgkwn96TcLToUMAEfDj+d2Y99tipOWprR0aERE1ICZpOyEWizGi02N486Hp8Hb2xOXMq5i1awH+unbY2qEREVEDYZK2MyGqtng/Zi66B0VCoyvEhwc/xcpDn0GjLbR2aEREVM84nrQdcpW7YHqvifjtyt/YcPxb7L16EBczrqBbUOdq103LSMW5k9crXe7l5I5+rXrCWeZUnyETEVEtMEnbKZFIhAFteiPEpw2WHViP6zk3sfX87pqtnH2qysX/O/MThrR/GIPaP8RkTURkRUzSdi7IPQALHpmNfVcP4U5xfrXlb968iaZNm1a6/PitMzinvoRNCduw/eJvTNZERFbEJN0IyCUyPNymd43KxhfEI6pDVKXLHw95FGfSL+J/Z35isiYisjImaTIjEokQ6heMTr7tmayJiKyMSZoqxGRNRGR9TNJUpeqS9WPtH0Ybr+bWDrNeJObfgPiWwtph2Cy2T9Ws0T6eSne09Gxm0TrJspikqUYqS9bfJmyzdmj169Yua0dg29g+VbNC+/QI6oJnIp+Cl7OHxeumhsckTffl3mT9S+I+aLQaa4dVL3Jyc+HepIm1w7BZbJ+qWbp9BADn1ZdxMPkYTqaexT/CYjGwbT+IxXxHVWPCJE21UpKsQ/2CrR1KvYmPj0dUVOV3vjs6tk/VrNE+GfmZ+OT4tzh68yQ2HP8ffkv8CxH+HdHWuyXaeLWAr4sPRCKRRWOi+sUkTURkp3xcvDCr9ws4cvMkPjm2CTdyb+FG7i3Tcje5C9p4tbibtI2J20PJsyH2hEmaiMjOdWsagXC/DjiTfhGJmVeRmHkNlzOvIrcoDydSz+JE6llTWR9nL7T1aom23i3QtEkAxPfR05ZL5AjxaQOJWNIQu0EVYJImImoEFFI5ugSGoktgKABAEARkFGTicknSvn0VV7KuI6MgExkFmTiYfKxW9YwOfwKPd3i0PkOnKjBJExE1QiKRCCoXb6hcvNGzmfFaucFgwM07qaaknZ6fUePtqQsycTM3FbcLshoqZKoAkzQRkYMQi8Vo5h6IZu6B6Neq532tu/vyXqyL/wY6Qd9A0VFFeK8+ERFVSywyXoc2GJikLYk9abIIQRDu/nt3uswyEQCx2HjzisEgwHBP2bKlpRKx6ZESrU4Pg1C67bJFJRIRZFLjHxW9QUBRse6eeEo/K+USSCTG76uFxTpodQaz5SXbl4hFcHWWm+bn5BVBEAAB5XfKSSGFUiE1bTNfo620fm93pWmfsnILjfWblRVM23R3VdzddwMysjV3qy1fv7eHExQy4/5n3ylCnqbYrM4SMqkY/t4upulrqbmAULqpsm2bX1j6x/lOQTHUWRqzMmU33zrQ3fQ7vZF2B4XFugrrb+IiN9VfWKzD1ZRcs/YRymy1ZUATOCtlAIBbGfnIzC00i6/kk0ImQfvmnqb5pxMzIAhCaf1l4ghUuULlaXy17e0cDa6l3jErU7b+yPa+pn06fzUTeRqtWf2Xbmqgd0qFj7sTWjd1BwAUFGpx+nKGWbVl26FTa280cTEeU0kpObiVkW/WjiUTTkopugT7mmb/dfImBINxfrtmHqbjoiqCIKCwWI87+cXILSiGTm9ASAsv0/Ldh66hoFALrc6AYq0BWp0exToDtDoDunfyh1RpPJ7Yk7asGiXpbdu2YfXq1dDpdBg/fjxGjx5ttvzKlSt4/fXXkZOTA5VKhSVLlsDd3b1BAibr0RtKkxUAfLrtDH7en4RirfE/bdk/Ls393PDRzP6m6eGzt0OnN1S43YnDQjG0TxsAwO/xN7Dsm+OVxrD53SGm5Dt39X6cu5pZYbn+XZsh7p9dAADJaXcwZfHvlW7z3Sm90bGVNwDgs5/OYvtfSRWWa+7vhpVl9umZ+btrtE9/n0yp8T4t/OxIjfYpRZ1X433a9OuFGu/TS0v2VrpPA6PcEf2g8fPhM6nV7pP47h3AK749UaN9SrtdgJkr9tVon7buS6zxPs3774Ea/Z5OXFTXeJ/Wb03A+WsVXJvde9tsn9RZGrz96eFKt/nelD5o0sqYKHcfulblPnUps08ffHXMbJ+aqlwQ0tILQb5ueDA8EAE+xi8+P+9Pws9/J+FOQTFy87Vm66g8nfDJq6U3gH3x8zlk5xVVWL9XEyWatjd+kTUYKm7LmtDq9BCLRKYvxacvZ+D8tUzk5BUjuIUn+nSufBhdR1Vtkk5LS8PSpUuxefNmyOVyjBw5Et27d0fbtm0BGL+d/fvf/8bcuXMRHR2NxYsXY82aNZg5c2aDB08Nx2AQkHo7H5duZONycjYu3cjGlZvZeH1CT3RqbfxDqZBLUFhc8bfqCjpNJiVPfFT04IdIJDJ9CShbtqR02V6IVCKGXCq+tyBEIuOystNOirKPjIjMVhGXqU8hk8DVSXbvJgGI4HK3F1fC3VVu+qMngvlOlfRiAUAuk8CrSdmejnn9ZXm4KUw9O/PqRaYeFwBIpWL4ezuX2aJ5/XJpaf0ergo0VbnArMDd+v28SrcBGL9c6QwGs5IlvXxneek23ZzlaBVY+rztvfWX1VTlCq1OX1ppmUjK1q+QSxDcwtNsedn6nRSlf678vV3Q8W5yu/dlHffuU2gbb+j1wr3VAwB83EsHiPFqokTn9qpK6y+7cyEtveBiOk6M83NycuDu7m7WLk4KKR7o6F+6zXvqd3UuPaZaBrijZ1gA7iUSAT4e5gPZPBgeCL3BgOy8Ily8no2b6nzcVBvHkw/wcTEl6XyNtvTsAAC5VAw3FzncnOXltvlI9+Yo1hogkxr/X0mlYshlEsilYrRr5olkbTYAICEpAyhzOVtTZDz7VHJ8Zt8pwo4DV5F9pxDZeUXIyStG9p1CZGQXoGhjMhZP64Pguz34/adSsP1v4xcTsViEkBZepuOfjESCUNFJqFJbtmzBkSNHsGDBAgDAypUrIQgCpkyZAgBISEjAa6+9hi1btgAA8vLykJubi8DAwPsOpqioCAkJCQgNDYVCUbsX1TvSW5EEQUBRsR5FWuOPTm+AXi9AbxCg0xsQ4O1i+kOSos5DSkY+Lly8hJatWkOvN0CnF2AwGCCXSRAdGQTA+B/u7U8OITE5G/mFunJ1Tn4yHIN6tQJgPN0rEolMdQDmf6PLJj974EjHTm2wfapmrfbR6Q24mpKLc1czkZ5VgL5dgtA2yPge79s5GuTkFcPNWQ43FxmU8tpf4dx//RiWHVgLfaYfWhQ9hNz8YmTnFaGoWI8HIwLx8rhuAIDU2/mYuODXCrchEYvw2nPdERXiBwA4lHALZ5Iycf5qJs5dzcRTD7fDuMEdax2jPaou71X7G0tPT4dKVfrt0tfXF6dOnTJNX79+HT4+PnjllVdw7tw5tG7dGq+99lqdgk5ISKjT+vHx8XVa39YIgoCth7NwLa0YrfwViH3A2NvIytNh+dbUStcb3c8H7QKVAIDfTuZg35m736j/vG1WromzBC6GNFNdicmZyC80wM1JjEAvufHHW4YALzlcFZmIj6/41GVj0NiOnfrG9qmaNdsn0Nn4k5OWiPg082X18dDU9bxrxg8iAy7dyDbNl0qArKws075r9QL6dHKDi1IMV6UELkoxXJQSuCrFUMrFQH4y4uOTjesCiAgEPKQSnLsK/PRXIkJUBWZn0xxdtUnaYDCYnU4SBMFsWqfT4fDhw/jyyy8RFhaGZcuWYdGiRVi0aFGtg2JP2lxWbiGOf20cXae1zNu0f1l3CiHfoYZCJoFCZjw9JRGLIZGIIBWLEdYpxHRqOke4jnz9TeTdyYW3lyckYhGkEjHEYuNp1KioUFN9b6gyofJwgre7Y512aozHTn1i+1StsbePKEUBpP6CNs3c8UxMb3i4KuDhpoCTQlrukkOPB8qvX1X7dBEE/HJqD5LT8+Ds1RKhbXwaYhdsUklPujLVJml/f38cPXrUNK1Wq+HrW3qXoUqlQosWLRAWFgYAGDJkCKZNm1aXmOkeeXfvDPb3dsb//SPSNN/TTYnvFw2p0Tb6d22O/l2b1+gPSdk7PomIAEByd3QtNxep6Qa++iISiRAV4oe0zAKkZOQ7VJKuTrXPSffq1QsHDhxAZmYmNBoNdu/ejejoaNPyyMhIZGZm4vz58wCAPXv2oFOnTg0XsQPKKzAmaXcX4zdXIiJLk4hKHmlsmEewnh7QHhvfGoRHu7dokO3bq2p70n5+foiLi8O4ceOg1WoxYsQIhIeHY+LEiZg2bRrCwsKwcuVKvPrqq9BoNPD398d7771nidgdRp6mGADg4iyrpiQRUcMo6Unrhdo/glWVsk8vUKka3eoXGxuL2NhYs3lr1641fY6IiMB3331Xv5GRScmLMFyVTNJEZB0SC71xTK83IL9Qx6R9F18LageUCinaNvNAU19Xa4dCRA6qZHjKhnzj2MGEWxg1bwfWb63bEz6NCV8Lagd6hAagR2j5lxwQEVmKRFT3N45VJ8DbBQWFOhy/kF7uSSJHxZ40ERFVyxI96eb+bvB2VyLrThGu3sptsHrsCZO0HSjS6mEwVPliOCKiBmWJnrRIJEJke+MjvsfOpzdYPfaESdoOLP7yKIbN2oqDCbesHQoROShL9KQBmARc4XwAAB6BSURBVEb7OnaBSRrgNWmbdfF6Fs5fy0RegRaJN3MgCOYDDBARWVLp3d0N15MGgIj2KohEwNmkTBQW6UxDvjoqx957G6Up0mH2R3+VG17P19O5kjWIiBpWSU9a38A96SYucrRr5oGL17NxJum2aTAOR8UkbYPyCozjvjopJBga3QZuznK09G9iGn6OiMjSSq5JN9Qbx8qaMDQMzkopmvu7NXhdto5J2ga5ucjwyjMPQCIRmY1FS0RkLeIGfuNYWR1acfyAEkzSNkgpl1Y4+DsRkbVIG/jd3VQx3t1NRETVEpuuSTd8TxoA9h2/ienL9mLXwasWqc9WMUnboPSsAnzzywX8Hn/D2qEQEQGwfE86r1CLSzeyEe/gz0szSdugtNsF+Grneew6eM3aoRARAYDpFZ0CBBgs0JsueV765CV1uSddHAmTtA25naPBnqPXsfmPywAAmZS/HiKyDSKRyPQYVkM/Kw0Afl7OaKoyvsv7wrWsBq/PVvHGMRuQmVuIVz/ejxtpd8zmBzf3tFJERETlSURi6KGHTtBDaoH0ERnsi5vqJBy/kI5Orb0bvD5b1OiT9NTFv8PNWQ6VpxN8PZ3h5+UElaczfD2d4ePhZNHeql5vwKXkbJy4qEbOnSI8PzwcAODhqkBOXhGUcglC2/igc3sVOrdXobkfnxEkItthfOuY1iI9acB4ynv7X0k4diEdYwZ1sEidtqZRJ+k8jbbKkVSmPBWBmB4tAQCnL2fg2IV0uDjJIAgCDIIAQQAEgwCFXIrhD7U1rbfp1wso0OhKy6C0bFQHP3TtYHxDTlJKDrbtuwJBAO4UFCMhMQP5hToAgEQswtjBHeCslEEsFmHRi73h7+3CU9xEZLMs9f7uEmFtfCCViHE5ORs5eUVwd1VYpF5b0qiTtJNCitWz+yM9SwN1VgHSszRIzyqAOkuDtMwC+HmVvmbz5GU1vttzqcLteLopzJL0z38nITO3qMKybi5yU5JWZ2vwy+HrZssDfFzQub0Kke1VkEpKE3Iz9pqJyMaVjoRlmSStVEgxdlAIVB7OUMglFqnT1jTqJC0RixDk64Yg3+oTYJdgX0glYhQW6SASiSASAWKRCCKRqNzAFk893B5FxXpTOZFIBPHdf9s39zCVaxnQBFOe6gyxCJDJJOjQ0svsiwERkT2xdE8aAIY/1M5iddmiRp2k70fHVt7o2KpmNyYM6d26RuV8PZ0R06NFXcIiIrIZlhhTmszxAigREdWIxMJvHStx+Gwqln1zrNwTMI6ASZqIiGpEYqX3d/99MgW/HbmBI2fTLFqvLWCSJiKiGikdCcuySTry7tvHjl9wvFeEMkkTEVGNWGskrMj2KohEwJmk2ygs1lm0bmtjkiYiohqx5JjSZbm7KtCmqTu0OgMSEm9btG5rY5ImIqIaseaY0o56yptJmoiIasTSY0qXVTIq1jEHS9J8TpqIiGpEWnK62wo96ZCWXghr44OOrb2gNwiQiEUWj8EamKSJiKhGxHdPd391agt2J/4JZ5nT3R9lmc9OcKpgWhCEOtUtlYixYPKD9bEbdoVJmoiIaiTAzRcnU88iKesGkrJu3Ne6YojgfOMbOMuUcLonuTvJlOgSEIougWENFLn9YpImIqIaeabzU+jbsgfyiwtQoNWU+Sk0m9aYTRdCo9WgWK9FXnE+8orzK9z2X9eOYMPwJdXGcO1WLo5fVCO2dytIJI3/tiomaSIiqhGxWIw2XrUbj+Dw0cMICetYPokXa7Dy8GfQaAshCAJEoqqvNb+z4TBuZeQjpIUnQlp61SoWe8IkTUREDU4ikqCJwhVNFK7llq0+8gUMggF6wWB6zKsyXYJ98VNGEo5dSHeIJN34zxUQEZFNk4mN/UWdofq3iTnao1hM0kREZFXSknGqa5CkQ9sYhxROTM6G3lC3O8btQY2S9LZt2zB48GA8+uij+Oqrryot98cff6B///71FhwRETV+0pKetL76JO2slMGriRI6vYCMbE1Dh2Z11SbptLQ0LF26FBs3bsQPP/yATZs24fLly+XKZWRk4N13322QIImIqPGSSkpOd9fsJSkBPi4AgFsZeQ0Wk62oNknv378fPXr0gIeHB5ydnRETE4OdO3eWK/fqq69iypQpDRIkERE1XtL7uCYNAIE+LnBzliFf0/hHxKr27u709HSoVCrTtK+vL06dOmVW5vPPP0fHjh0RERFRL0ElJCTUaf34+Ph6iaOxYvtUjm1TNbZP1dg+VausfXRFWgDAidOnoFJ4VrudHq0FPNjWD9DdQnz8rXqN0dZUm6QNBoPZc2v3Psd28eJF7N69Gxs2bEBqamq9BBUaGgqFQlGrdePj4xEVFVUvcTRGbJ/KsW2qxvapGtunalW1z6aMnbidnY3gDsFo5dnMwpFZV1FRUZUd02pPd/v7+0OtVpum1Wo1fH19TdM7d+6EWq3Gk08+iUmTJiE9PR2jRo2qY9hEROQo7vd0dwkD7+4GevXqhQMHDiAzMxMajQa7d+9GdHS0afm0adOwa9cu/Pjjj1izZg18fX2xcePGBg2aiIgaj5JHsLQ1uLsbALQ6PV5Y9CtGvfZzo0/U1SZpPz8/xMXFYdy4cRg2bBiGDBmC8PBwTJw4EadPn7ZEjERE1IjJJPfXk5ZJJcjX6JBfqENmbmFDhmZ1NXotaGxsLGJjY83mrV27tly5oKAg7Nmzp34iIyIih1B6urvm41QH+LggO68ItzLy4ePh1FChWR3fOEZERFYlqcU16ZJnpVMa+bPSTNJERGRV9/Pu7hKBKmOS/t9vl3Ao4RYEoXFem2aSJiIiqyp9d3fNT3c/2r0Fmvm5Ii2zAG9/ehiXk7MbKjyr4lCVRERkVSXXpLV6bY3X8XRT4sMZD+Hn/UlIupmLds1KX4JSrNVDLqt6yEt7wSRNRERWJavFjWMAIJWIMbRPG7N5F69nYf76gxg9sAMe7d4CErGokrXtA093ExGRVd3PUJXV+eNYMnLyirHqu5OIW/oHTl/OqPM2rYlJmoiIrKpkFCxtPSTpiY+H4uVx3eDr6YSklFy8svpvLPrsCNIyC+q8bWtgkiYiIquqzXPSlRGJRHgwIhCrZj+M0QNDIJdJ8PepFPz73d9w4LT9DcbBJE1ERFZV23d3V0Uhk2DkI8H4ePbD6BsZBJlUjJAW1Y+wZWt44xgREVlVfV6TvpfK0wn/GROFrDuF8HRTGuvRG7Di2xOI7d0abZt51Hud9YlJmoiIrMp0d3cNB9iojZIEDQC7D13DnqM38Hv8DQzo1hxjB3WAZxNlFWtbD093ExGRVdXnNema6NclCE/0awuJWIRfDl/H84t+w+bfL0OrM1ik/vvBJE1ERFZlGqqyAU53V8RZKcOzsZ3w0cz+6NbRD5oiHT7dfgZT3t+DYxfSLRJDTfF0NxERWZVMIgPQMNekq9JU5Yp5z/VA/Pk0rPsxAcnpecjI1lg0huowSRMRkVXV5t3d9SkqxA8R7VTYeywZ/aKameYfO5+O9i084eoks0pcAJM0ERFZmend3RbuSZvFIBHj4W7NTdPpWQV4Z8NhKOUSjB3UAY9Y6RWjvCZNRERWVZKk9VZM0vfS6gxo18wDufnFWPndSUxfuhcJiZZ/xSiTNBERWVVDPiddW01Vrlg4+UHMHtcVKk8nXEnJwZxVf+Pdz48gJ6/IYnHwdDcREVlV6VCVtpOkAeMrRntHNEXXDn7Y8kcivttzCRevZ0GpsFzqZJImIiKrkkks+5z0/VLKpfjno8EY0K05budooLDgWNVM0kREZFUN8e7uhqDydILK08midfKaNBERWZWlX2ZiT5ikiYjIqmQWfi2oPWGSJiIiq7KX093WwCRNRERWZXoEy8bu7rYFTNJERGRVUgl70pVhkiYiIquy9FCV9oRJmoiIrIrXpCvHJE1ERFYlEYkhggh6wQCDYLB2ODaFSZqIiKxKJBJZfbhKW8UkTUREVldyyrtYV2zlSGwLkzQREVmdv6sKAHAt56aVI7EtTNJERGR1wT5tAAAXMhKtHIltYZImIiKrC1a1BgCcV1+2ciS2pUZJetu2bRg8eDAeffRRfPXVV+WW//rrr3j88ccxdOhQTJ48GTk5OfUeKBERNV4hPm0BABdvJ8Fg4B3eJapN0mlpaVi6dCk2btyIH374AZs2bcLly6XfdPLy8vDGG29gzZo12Lp1K4KDg7FixYoGDZqIiBoXb2dP+Dh7oUCrwY3cFGuHYzOqTdL79+9Hjx494OHhAWdnZ8TExGDnzp2m5VqtFq+//jr8/PwAAMHBwbh161bDRUxERI1SsI/xlDevS5eSVlcgPT0dKpXKNO3r64tTp06Zpj09PfHII48AAAoLC7FmzRqMHTu2TkElJCTUaf34+Pg6rd/YsX0qx7apGtunamyfqlXXPs4aOQBg/4Uj8M5xtURINq/aJG0wGCASiUzTgiCYTZe4c+cOXnzxRYSEhOCJJ56oU1ChoaFQKBS1Wjc+Ph5RUVF1qr8xY/tUjm1TNbZP1dg+VatJ+3hn+eGX3fuhNmQ5TFsWFRVV2TGt9nS3v78/1Gq1aVqtVsPX19esTHp6OkaNGoXg4GC88847dQiXiIgcVXP3QDjJlFAXZCKzINva4diEapN0r169cODAAWRmZkKj0WD37t2Ijo42Ldfr9XjhhRcwaNAgzJ07t8JeNhERUXXEYjHae999FIvXpQHU4HS3n58f4uLiMG7cOGi1WowYMQLh4eGYOHEipk2bhtTUVJw9exZ6vR67du0CYDxdzR41ERHdr2CfNjiZehbnMy6jV3PHOOVdlWqTNADExsYiNjbWbN7atWsBAGFhYTh//nz9R0ZERA4nhG8eM8M3jhERkc1o690SYpEYV7OTodEWWjscq2OSJiIim6GUKtDKsxkEQcCl20nWDsfqmKSJiMimcLCNUkzSRERkU0quS/MObyZpIiKyMSU96Uu3k6A36K0cjXUxSRMRkU3xdHKHn4sPCnVFuJ7j2INtMEkTEZHNCVbdPeXt4ONLM0kTEZHN4fPSRkzSRERkc4LL3DwmCIKVo7EeJmkiIrI5TZv4w0XujExNNjIKMq0djtUwSRMRkc0Ri8QIvjvYhiOf8maSJiIim2Q65a1mkiYiIrIpISrePMYkTURENqmNZwtIxBJcz0lBfnGBtcOxCiZpIiKySXKpHG08W0CA4w62wSRNREQ2K9jHePOYo77Hm0maiIhslqOPiMUkTURENiukzGAbOgccbINJmoiIbFYTpRsC3HxRrNfiatYNa4djcUzSRERk00J82gJwzOvSTNJERGTTHPm6NJM0ERHZtJAyd3g72mAbTNJERGTTAtz84KZwRU5hLtLyM6wdjkUxSRMRkU0TiUSlp7wd7D3eTNJERGTzQhz0pSZM0kREZPNK7vB2tJvHmKSJiMjmtfJsBplYiuTcW8gryrd2OBbDJE1ERDZPJpGhrXdLAMCF21esG4wFMUkTEZFdcMTnpZmkiYjILpQk6fPqy1aOxHKYpImIyC4Eexvv8E7MvAatXmvlaCyDSZqIiOyCq8IFQU0CoDXokOQgg20wSRMRkd0oGbryfIZjnPJmkiYiIrtRcl36nINcl5ZaOwAiIqKaClEZk3R8ymnM3rUAfm4qKCUKKKRyKKQKKKVyKO5OK6V350vkd/+9u/zufKVEAanEttNgjaLbtm0bVq9eDZ1Oh/Hjx2P06NFmy8+dO4e5c+ciPz8fXbt2xZtvvgmp1LZ3nIiI7I+fqwpPdhyMnZd+R1L2DSRl1+3atEQkNkva9yZ7uVQOpaQ0sTtJlXggqDMC3HzraY+qVm0mTUtLw9KlS7F582bI5XKMHDkS3bt3R9u2bU1lZs6cibfffhudO3fGK6+8gm+//RajRo1q0MCJiMgx/SMsFk90iMFZ9WXka/NRpCtGoa4IRbpiFOmLUKTTokhXhEJ9MYpK5t8zXag3/qsXDCjQalCg1dS4/rPqS5gT/WID7mGpapP0/v370aNHD3h4eAAAYmJisHPnTkyZMgUAcPPmTRQWFqJz584AgOHDh+PDDz+sVZIuGSe0uLj4vtctq6ioqE7rN3Zsn8qxbarG9qka26dq9d0+Hbza1Gl9QRCgN+hRrC9Gkb4YRXotinXFKDYY/y3SF6NYp0WxoRhFOi2K9cXQGnTo7N+p3valJN9VNk52tUk6PT0dKpXKNO3r64tTp05VulylUiEtLa1WwWq1xufeLl68WKv1SyQkJNRp/caO7VM5tk3V2D5VY/tUzd7aRwZABhlcIDObfyc5CwnJWfVal1arhVKpLDe/2iRtMBggEolM04IgmE1Xt/x+uLi4oH379pDJZLXeBhERkb0QBAFarRYuLi4VLq82Sfv7++Po0aOmabVaDV9fX7PlarXaNJ2RkWG2/H6IxWK4ubnVal0iIiJ7VFEPukS1z0n36tULBw4cQGZmJjQaDXbv3o3o6GjT8qZNm0KhUCA+Ph4A8OOPP5otJyIiotoRCZVdrS5j27Zt+O9//wutVosRI0Zg4sSJmDhxIqZNm4awsDCcP38er776KvLy8tCpUycsXLgQcrncEvETERE1WjVK0kRERGR5fC0oERGRjWKSJiIislFM0kRERDaKSZqIiMhGMUkTERHZKCZpIiIiG8UkTUREZKOYpImIiGyUXSbplJQUjB49GgMHDsS///1v5OfnV1r277//xvjx403TWq0WXbp0weOPP2760ev1lgjbIurSNoIg4N1338XAgQMxePBg06teG5OatE9xcTFmzpyJQYMG4YknnkBiYiKAxn3sbNu2DYMHD8ajjz6Kr776qtzyc+fOYfjw4YiJicHcuXOh0+kA3N/xZs9q2z5btmxB7969TcfL0qVLLR26RVTXPiVmzZqFzZs3m6Yd5fipE8EOTZo0Sdi+fbsgCILw0UcfCe+99165Mnq9Xli/fr3wwAMPCGPGjDHNP336tPDss89aLFZLq0vb7NixQ5g4caKg1+uFK1euCI888oig1WotFrsl1KR91q1bJ7z22muCIAjC4cOHhaeeekoQhMZ77KSmpgoPPfSQkJWVJeTn5wuxsbHCpUuXzMo89thjwvHjxwVBEIQ5c+YIX331lSAINWtPe1eX9pk/f76wbds2i8dsSTVpn9TUVOH5558XwsPDhe+//9403xGOn7qyu560VqvFkSNHEBMTAwAYPnw4du7cWa5cYmIiEhMT8dZbb5nNP336NDIzMzF8+HA8/fTTOHz4sEXitoS6ts3evXsxePBgiMVitGrVCgEBATh+/LhFYreEmrbPH3/8gaFDhwIAunXrhszMTKSkpDTaY2f//v3o0aMHPDw84OzsjJiYGLN2uXnzJgoLC9G5c2cApe1W0/a0d7VtH8D492bLli2IjY3Ff/7zH+Tk5FhlHxpSde0DGHvaDz/8MAYNGmSa5yjHT13ZXZLOysqCq6srpFLjKJsqlQppaWnlyrVr1w7vvPMO3N3dzeaLRCI8/PDD2LRpE9544w3ExcUhMzPTIrE3tLq2TXp6utkwoyqVCqmpqQ0btAXVtH3S09OhUqlM0yXt0FiPnXv319fX16xdKmqPtLS0Grenvatt+5R8njx5MrZu3YqAgADMnz/fcoFbSHXtAwATJkzAU089ZTbPUY6fuqp2PGlr2rFjBxYuXGg2r0WLFhCJRGbz7p2uysiRI02fO3bsiPDwcBw7dgwDBgyoW7AW1hBtYzAYzMoLggCx2O6+xwGoW/sIglBhOzSWY+deFf3ey05XtvzecsD9HW/2orbtAwArV640zZ8wYQIeeeQRC0RsWdW1T2Uc5fipK5tO0oMGDTI7PQIYT5F0794der0eEokEarXarPdXnR9++AFdunRB8+bNARgPFJlMVq9xW0JDtI2/vz/S09NN0xkZGfe1vi2pS/v4+fkhPT3ddIyUtENjOXbu5e/vj6NHj5qm720Xf39/qNVq03RJe3h5eeHOnTu1Pt7sRW3b586dO/j+++/xzDPPADAeLxKJxGJxW0p17VMZRzl+6sruukkymQxdu3bFzz//DMCYdKOjo2u8/oULF/DJJ58AAK5cuYJz584hKiqqQWK1tLq2TXR0NLZt2wa9Xo9r167h6tWrCAsLa6hwLa6m7dO3b1/8+OOPAICjR49CoVAgMDCw0R47vXr1woEDB5CZmQmNRoPdu3ebtUvTpk2hUChMd/v/+OOPiI6OrvPxZi9q2z7Ozs5Yt24dTp48CQD48ssvG2VPurr2qYyjHD91ZpXb1eooOTlZGDNmjDBo0CDh2WefFbKzswVBEISNGzcKy5YtMyt78OBBszuY79y5I0ydOlV47LHHhCFDhggHDhywaOwNrS5tYzAYhEWLFgmDBw8WBg8eLOzbt8+isVtCTdqnsLBQmDVrljB48GBh2LBhQkJCgiAIjfvY2bp1q/DYY48Jjz76qLBmzRpBEARhwoQJwqlTpwRBEIRz584JTz75pBATEyNMnz5dKCoqEgSh8vZsbGrbPkeOHBGGDRsmDBw4UHjhhReE3Nxcq+1DQ6qufUrMnj3b7O5uRzl+6kIkCIJg7S8KREREVJ7dne4mIiJyFEzSRERENopJmoiIyEYxSRMREdkoJmkiIiIbxSRNZEUvv/wygoODq/wpKWOt9xoHBwdj/fr1ddrGoUOHEBwcjNOnT1daZvPmzQgODm4Ur1olqi82/cYxosZu8uTJZq8bnT17Nlq0aIHJkyeb5ul0OmzZssUa4RGRlTFJE1lR8+bNTa8ZBQClUgkvLy/TiEoAkJycbI3QiMgG8HQ3kZ24efMmJk6ciIiICPTu3RurV682LSs5nfzNN9+gd+/e6Nu3rym5b9++HbGxsQgLC8OAAQPwxRdfmG335MmTGD16NCIjI/HAAw9g2rRpuHnzplmZ7OxsTJ8+HZGRkejevTsWLFgArVZrWp6fn493330X/fv3R3h4OEaMGIG//vqryv354YcfEBMTg/DwcEycOBHZ2dl1bSKiRodJmshOLF26FOHh4fj444/x0EMPYdmyZdizZ49ZmVWrVmH+/PmIi4tDUFAQtmzZghkzZqBbt25YvXo1hg0bhoULF2LdunUAAI1Gg0mTJsHPzw+rVq3CW2+9hbNnz2L69Olm2123bh08PT2xatUqjBw5Ep999hm++eYbAMZRkCZMmIDNmzdj0qRJWLFiBQIDAzFp0iTs27evwn3ZsWMHZs+ejQcffBArV65Es2bNsGTJkgZoNSL7xtPdRHZi+PDhmDp1KgCgW7du2LVrFw4dOoT+/fubyowfP940bTAYsGTJEsTGxmLevHkAgN69e0MkEmHVqlUYNWoULl++jOzsbIwdOxaRkZEAAE9PTxw8eBAGg8E0VGmvXr3w2muvAQB69uyJPXv24NChQxg7diz++OMPHDt2DOvWrUOfPn0AGAcp+cc//oGlS5ea5pW1Zs0a9OnTxxRXnz59kJKSgt9//70hmo7IbrEnTWQnunTpYvoslUrh5+eH3NxcszJt27Y1fU5KSkJ6ejr69esHnU5n+omOjkZ+fj5OnTqF1q1bw8PDAy+88ALmz5+PvXv3onPnzpg2bZrZWOIlCbxE06ZNTXUfOXIELi4u5ZLx4MGDcebMGeTl5ZnN12g0OHfuXLkRj2JiYmrRKkSNG5M0kZ1QKpVm02KxGPeOj+Pl5WX6XHKNd8aMGejUqZPpZ8SIEQCM4/66urriyy+/RM+ePbFlyxZMmjQJDz74IDZu3Gi2XScnp0rrzs3NhY+PT7l4S+bl5+ebzc/NzYUgCPD09KywPBGV4uluokbKzc0NADBv3jyEh4eXWx4UFAQAaNeuHZYtW4bi4mLEx8fjs88+w5tvvolOnTohIiKi2nrc3d2RkZFRbr5arQYAeHh4lCsvEolw+/Zts/m8cYyoPPakiRqpklPZaWlpCAsLM/1kZ2dj+fLlyMvLw59//omePXsiMzMTcrkcPXv2NF17TklJqVE9UVFRyM/PL3eT2I4dO9CpUycoFAqz+UqlEp07d8avv/5qNn/v3r112Fuixok9aaJGSiqVYurUqVi0aBEA4w1fycnJ+OCDD9CyZUsEBQXBzc0NgiBgypQpmDhxImQyGT777DM0adIE3bt3r1E9/fr1Q0REBGbOnIm4uDgEBARg8+bNOHnyJD7++OMK15k6dSomTJiAOXPmYPDgwTh48GC5pE1ETNJEjdqYMWOgVCqxYcMGfPLJJ/Dw8MDAgQMRFxcHkUgEDw8PrFu3Dh988AFmzZoFrVaL8PBwfPrpp2bXt6sikUiwbt06LF68GEuXLoVGo0GHDh2wZs2acjeHlXjwwQexYsUKLF++HNu3b0dYWBhmzpyJ+fPn1+fuE9k9kXDvnSdERERkE3hNmoiIyEYxSRMREdkoJmkiIiIbxSRNRERko5ikiYiIbBSTNBERkY1ikiYiIrJRTNJEREQ26v8BLcnQTTcF7YIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression Precision Recall\n",
    "\n",
    "# To decide on a threshold, look at decision scores\n",
    "y_scores = cross_val_predict(log_reg, X_train, y_train, cv=3, method=\"decision_function\")\n",
    "\n",
    "# Compute precision and recall for all possible thresholds\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train, y_scores)\n",
    "\n",
    "# Plot ROC\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "# plt.xlim([-400000, 400000])\n",
    "# save_fig(\"precision_recall_vs_threshold_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 13: Logistic Regression Precision-Recall Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot precision directly against recall for Logistic Regression\n",
    "\n",
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, \"b-\", linewidth=2)\n",
    "    plt.xlabel(\"Recall\", fontsize=16)\n",
    "    plt.ylabel(\"Precision\", fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "# save_fig(\"precision_vs_recall_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 14: Logistic Regression ROC Curve - Train Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve for Logistic Regression\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_scores)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "y_scores = cross_val_predict(log_reg, X_train, y_train, cv=10)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('Receiver Operating Characteristic, Test Set, Logistic Regression\\nROC curve (area = %0.2f)' % roc_auc)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "# save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 15: Naive Bayes (Bernoulli) ROC Curve - Train Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve for Naive Bayes\n",
    "y_scores = cross_val_predict(bern_clf, X_train, y_train, cv=10)\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_scores)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('Receiver Operating Characteristic, Test Set, Naive Bayes\\nROC curve (area = %0.2f)' % roc_auc)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "# save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 16: SVC ROC Curve - Train Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve for SVC\n",
    "y_scores = cross_val_predict(svc_clf, X_train, y_train, cv=10)\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_scores)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('Receiver Operating Characteristic, Test Set, SVC\\nROC curve (area = %0.2f)' % roc_auc)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "# save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate models on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "lr_pred_test = log_reg.predict_proba(X_test)\n",
    "lr_pred_test = [p[1] for p in lr_pred_test]\n",
    "lr_pred_class_test = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive Bayes\n",
    "bern_pred_test = bern_clf.predict_proba(X_test)\n",
    "bern_pred_test = [p[1] for p in bern_pred_test]\n",
    "bern_pred_class_test = bern_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVC\n",
    "svc_pred_test = svc_clf.predict_proba(X_test)\n",
    "svc_pred_test = [p[1] for p in svc_pred_test]\n",
    "svc_pred_class_test = svc_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: \n",
      "\n",
      "Area Under the Curve:  0.6097651605231867\n",
      "\n",
      " Confusion Matrix: \n",
      " [[72 44]\n",
      " [47 69]]\n",
      "\n",
      " PPV, Correctly Classifies Response Yes: \n",
      " 0.5948275862068966\n",
      "\n",
      " NPV, Correctly Classifies Response No: \n",
      " 0.6206896551724138\n",
      "-------------------\n",
      "Naive Bayes (Bernoulli): \n",
      "\n",
      "Area Under the Curve:  0.6064209274673009\n",
      "\n",
      " Confusion Matrix: \n",
      " [[72 44]\n",
      " [47 69]]\n",
      "\n",
      " PPV, Correctly Classifies Response Yes: \n",
      " 0.5948275862068966\n",
      "\n",
      " NPV, Correctly Classifies Response No: \n",
      " 0.6206896551724138\n",
      "-------------------\n",
      "SVC: \n",
      "\n",
      "Area Under the Curve:  0.5816736028537455\n",
      "\n",
      " Confusion Matrix: \n",
      " [[66 50]\n",
      " [44 72]]\n",
      "\n",
      " PPV, Correctly Classifies Response Yes: \n",
      " 0.6206896551724138\n",
      "\n",
      " NPV, Correctly Classifies Response No: \n",
      " 0.5689655172413793\n"
     ]
    }
   ],
   "source": [
    "# Evaluate\n",
    "print(\"Logistic Regression: \\n\")\n",
    "metrics(y_test, lr_pred_test, lr_pred_class_test)\n",
    "print('-------------------')\n",
    "print(\"Naive Bayes (Bernoulli): \\n\")\n",
    "metrics(y_test, bern_pred_test, bern_pred_class_test)\n",
    "print('-------------------')\n",
    "print(\"SVC: \\n\")\n",
    "metrics(y_test, svc_pred_test, svc_pred_class_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model is a little better than random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 17: Logistic Regression ROC Curve - Test Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve for Logistic Regression\n",
    "fpr, tpr, thresholds = roc_curve(y_test, lr_pred_test)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot curve\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('Receiver Operating Characteristic, Test Set, Logistic\\nROC curve (area = %0.2f)' % roc_auc)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "# save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 18: Naive Bayes (Bernoulli) ROC Curve - Test Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve for Naive Bayes\n",
    "fpr, tpr, thresholds = roc_curve(y_test, bern_pred_test)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot curve\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('Receiver Operating Characteristic, Test Set, Naive Bayes\\nROC curve (area = %0.2f)' % roc_auc)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "# save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 19: SVC ROC Curve - Test Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve for SVC\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc_pred_test)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot curve\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('Receiver Operating Characteristic, Test Set, SVC\\nROC curve (area = %0.2f)' % roc_auc)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "# save_fig(\"roc_curve_plot\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}