{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CMSE 802 Project \n",
    "# The Behavioral Analysis of Predictive Modeling on a Bank Marketing Dataset.\n",
    "\n",
    "## Author: Jamell Dacon \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Abstract\n",
    "\n",
    "        I propose a data mining supervised learning approach to predict the accuracy of several models of the client subscription of a term deposit to a bank via information collected via a telephone call; without pruning the data collected from UCI Machine Learning Repository. The largest dataframe was analyzed with 20 predictor variables and a target variable comprising of 41188 examples. The individual comparison of 4 models i.e. Gradient Boosting (GB), Random Forest (RF), Multi-Layer Perceptron (MLP) and Logistic Regression (LGR) models were made along with an 8 model cross-validation comparison comprising of Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machine (SVM), Naive Bayes (NB) including the individual model comparison models. Using three metrics such as classification report, accuracy score and a confusion matrix. Both the GB and MLP classifer models produced the best results with an accuracy score of 91.90% and 90.97% respectively, tested on 40% of the data. In the 8 model comparison 10-fold cross validation was implemented were only two models, namely the Gradient Boosting (GB) and Linear Discriminant Analysis (LDA) models produced the best mean accuracy results accomplishing over 90%, whereas the mean of the RF model's accuracy decreased by 1.43%.\n",
    "    \n",
    "    Key words: Supervised, Accuracy score, Cross-validation\n",
    "    \n",
    "    \n",
    "\n",
    "## 1.  Introduction\n",
    "    \n",
    "          Banks are a cruicial part of our economy and may be considered the safest way of saving money over a long-period of time. The commercializing of banks is ubiquitious, meaning it can be found everywhere. One of the many ways to attract customers is by initializing a relationship with a customer with the goal of that customer becoming a client of the bank. This relationship is created by a 1-1 telephone call between the telemarketer and the potential client. With the information received from each potential customer, a data mining approach will be undertaken to gain insight from the information retrieved i.e the available examples to have knowledge of the prediction of a subscription of a client to the bank using classification and regression models. There are several supervised models used such as Gradient Boosting (GB), Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression (LGR) Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machine (SVM) and Naive Bayes (NB).\n",
    "        \n",
    "          In this paper, I propose analytical predictive modeling insight of the behavior of several algorithm on predicting the accuracy result of a phone call to a client from a bank telemarketer attemptiong to sell long term deposits by using supervised learning. The main contributions of this work are:\n",
    "        • A focus on Data Exploration i.e Univariate Analysis was conducted on the largest dataset consisting of 41188 examples with 20 attributes. In the modeling phase, the feature distribution was plotted to gain insight of the visualization of each feature. Futhermore, as over 50% of the data was categorical, encoding allowed the data to be fully numerical which was then passed into models.\n",
    "        • Several individual model comparisons on a 60/40 split. \n",
    "        • A comparison of eight supervised models (GB, RF, LDA, KNN, LGR, SVM, DT and NB). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.  Methods and Materials\n",
    "\n",
    "### 2.1. Bank telemarketing dataset information \n",
    "\n",
    "There are four datasets: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing\n",
    "\n",
    "- **bank-additional-full.csv** with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010)\n",
    "\n",
    "- **bank-additional.csv** with 10% of the examples (4119), randomly selected from **bank-additional-full.csv**, and 20 inputs.\n",
    "\n",
    "- **bank-full.csv** with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). \n",
    "\n",
    "- **bank.csv** with 10% of the examples and 17 inputs, randomly selected from **bank-full.csv** (older version of this dataset with less inputs). \n",
    "\n",
    "The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). However, I proposed to carry about this project using the **bank-additional-full.csv** (largest dataset) since the goal of this project is the efficiently and accurately predict the output using each supervised method to attain knowledge of the robustness of each algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Data mining tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in models and packages necessary for datasets\n",
    "# Importing individual libraries make it easy for us to use them without having to call the parent libraries\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Visualization libraries\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Managing Warnings i.e. Deprecation warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Bank telemarketing data: Attribute Information  \n",
    "\n",
    "**Input variables:**\n",
    "\n",
    "**i. Bank client data:**\n",
    "1.  $\\underline{age}$ (numeric)\n",
    "2.  $\\underline{job}$: type of job (categorical): 'admin.','bluecollar','entrepreneur','housemaid','management','retired','selfemployed','services','student','technician','unemployed','unknown')\n",
    "3.  $\\underline{marital}$: marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "4.  $\\underline{education}$ (catgorical): 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "5.  $\\underline{default}$: has credit in default? (categorical: 'no','yes','unknown')\n",
    "6.  $\\underline{housing}$: has housing loan? (categorical: 'no','yes','unknown')\n",
    "7.  $\\underline{loan}$: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "**ii. Related with the last contact of the current campaign:**\n",
    "8.  $\\underline{contact}$: contact communication type (categorical: 'cellular','telephone') \n",
    "9.  $\\underline{month}$: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "10.  $\\underline{day \\ of \\ week}$: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "11.  $\\underline{duration}$: last contact duration, in seconds (numeric). \n",
    "\n",
    "Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "\n",
    "**iii. Other attributes:**\n",
    "12.  $\\underline{campaign}$: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "13.  $\\underline{pdays}$: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "14.  $\\underline{previous}$: number of contacts performed before this campaign and for this client (numeric)\n",
    "15.  $\\underline{poutcome}$: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "**iv. Social and economic context attributes**\n",
    "16.  $\\underline{emp.var.rate}$: employment variation rate - quarterly indicator (numeric)\n",
    "17.  $\\underline{cons.price.idx}$: consumer price index - monthly indicator (numeric) \n",
    "18.  $\\underline{cons.conf.idx}$: consumer confidence index - monthly indicator (numeric) \n",
    "19.  $\\underline{euribor3m}$: euribor 3 month rate - daily indicator (numeric)\n",
    "20.  $\\underline{nr.employed}$: number of employees - quarterly indicator (numeric)\n",
    "\n",
    "**Output variable (desired target):**\n",
    "21.  $\\underline{y}$ - has the client subscribed a term deposit? (binary: 'yes','no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5. Data Exploration - Univariate Analysis\n",
    "When exploring our dataset and its features, we have many options available to us. Here we will explore each feature individually, or compare pairs of features, finding the correlation between multiple features is called Multivariate analysis.\n",
    "\n",
    "Here we explore some simple Univariate (a single feature) analysis.\n",
    "Features can be of multiple types:\n",
    "- **Nominal**: is for mutual exclusive, but not ordered, categories.\n",
    "- **Ordinal**: is one where the order matters but not the difference between values.\n",
    "- **Interval**: is a measurement where the difference between two values is meaningful.\n",
    "- **Ratio**: has all the properties of an interval variable, and also has a clear definition of 0.0.\n",
    "\n",
    "There are multiple ways of manipulating each feature type, but for simplicity, we'll define only two feature types i.e. attributes of features:\n",
    "- **Numerical**: any feature that contains numeric values.\n",
    "  - Nominal \n",
    "  - Ordinal \n",
    "- **Categorical**: any feature that contains categories, or text.\n",
    "  - Interval\n",
    "  - Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.1 Univariate Analysis of the Bank Additional Full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
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       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the bank-additional-full.csv dataset \n",
    "bank_additional_full_df = pd.read_csv('bank-additional-full.csv',sep=';')\n",
    "\n",
    "# We can view the first 5 rows of the dataframe (df) \n",
    "bank_additional_full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           0\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          0\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp.var.rate      0\n",
       "cons.price.idx    0\n",
       "cons.conf.idx     0\n",
       "euribor3m         0\n",
       "nr.employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We first check the df for missing values i.e. null entries\n",
    "bank_additional_full_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null object\n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# We can now get the information of each row in the df i.e data types and null entries of each column\n",
    "bank_additional_full_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have an idea of the attributes types of each column, hence we can then do some analysis on the numerical and categorical columns of the df, along with some visualization those columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
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       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
       "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
       "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can now get the description of each numerical column in the df\n",
    "bank_additional_full_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>thu</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10422</td>\n",
       "      <td>24928</td>\n",
       "      <td>12168</td>\n",
       "      <td>32588</td>\n",
       "      <td>21576</td>\n",
       "      <td>33950</td>\n",
       "      <td>26144</td>\n",
       "      <td>13769</td>\n",
       "      <td>8623</td>\n",
       "      <td>35563</td>\n",
       "      <td>36548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           job  marital          education default housing   loan   contact  \\\n",
       "count    41188    41188              41188   41188   41188  41188     41188   \n",
       "unique      12        4                  8       3       3      3         2   \n",
       "top     admin.  married  university.degree      no     yes     no  cellular   \n",
       "freq     10422    24928              12168   32588   21576  33950     26144   \n",
       "\n",
       "        month day_of_week     poutcome      y  \n",
       "count   41188       41188        41188  41188  \n",
       "unique     10           5            3      2  \n",
       "top       may         thu  nonexistent     no  \n",
       "freq    13769        8623        35563  36548  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can get the description of each categorical column in the df\n",
    "bank_additional_full_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.2 Feature distribution i.e. visualization of each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2520 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let’s plot the distribution of each feature\n",
    "\n",
    "def plot_distribution(dataset, cols=5, width=20, height=15, hspace=0.2, wspace=0.5):\n",
    "    plt.style.use('seaborn-whitegrid')\n",
    "    fig = plt.figure(figsize=(width,height))\n",
    "    fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=wspace, hspace=hspace)\n",
    "    rows = math.ceil(float(dataset.shape[1]) / cols)\n",
    "    for i, column in enumerate(dataset.columns):\n",
    "        ax = fig.add_subplot(rows, cols, i + 1)\n",
    "        ax.set_title(column)\n",
    "        if dataset.dtypes[column] == np.object:\n",
    "            g = sns.countplot(y=column, data=dataset)\n",
    "            substrings = [s.get_text()[:21] for s in g.get_yticklabels()]\n",
    "            g.set(yticklabels=substrings)\n",
    "            plt.xticks(rotation=45)\n",
    "        else:\n",
    "            g = sns.distplot(dataset[column])\n",
    "            plt.xticks(rotation=25)\n",
    "    \n",
    "plot_distribution(bank_additional_full_df, cols=3, width=20, height=35, hspace=0.7, wspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.3 Model Preparation: Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: yHat, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can create a dummy column for our target variable y called yHat to have a count of the target variable.\n",
    "\n",
    "yHat ={'yes' : 1, 'no' : 0}\n",
    "bank_additional_full_df['yHat'] = bank_additional_full_df['y'].map(lambda x: yHat[x])\n",
    "bank_additional_full_df['yHat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "      <th>yHat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...   pdays  previous     poutcome  emp.var.rate  \\\n",
       "0   may         mon  ...     999         0  nonexistent           1.1   \n",
       "1   may         mon  ...     999         0  nonexistent           1.1   \n",
       "2   may         mon  ...     999         0  nonexistent           1.1   \n",
       "3   may         mon  ...     999         0  nonexistent           1.1   \n",
       "4   may         mon  ...     999         0  nonexistent           1.1   \n",
       "\n",
       "  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  yHat  \n",
       "0         93.994          -36.4      4.857       5191.0  no     0  \n",
       "1         93.994          -36.4      4.857       5191.0  no     0  \n",
       "2         93.994          -36.4      4.857       5191.0  no     0  \n",
       "3         93.994          -36.4      4.857       5191.0  no     0  \n",
       "4         93.994          -36.4      4.857       5191.0  no     0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we can view the first 5 rows of the df. However we now see that yHat is an encoded column of y\n",
    "bank_additional_full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can further do some analysis by encoding the categorical data to be numerical  \n",
    "\n",
    "for col in bank_additional_full_df.columns:\n",
    "    if bank_additional_full_df[col].dtype == object:\n",
    "        bank_additional_full_df[col] = bank_additional_full_df[col].astype('category')\n",
    "        bank_additional_full_df[col] = bank_additional_full_df[col].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check to see that it worked (with the exception of the yHat dummy variable as it should be the same as y encoded):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "0   56    3        1          0        0        0     0        1      6   \n",
       "1   57    7        1          3        1        0     0        1      6   \n",
       "2   37    7        1          3        0        2     0        1      6   \n",
       "3   40    0        1          1        0        0     0        1      6   \n",
       "4   56    7        1          3        0        0     2        1      6   \n",
       "\n",
       "   day_of_week ...  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "0            1 ...         1    999         0         1           1.1   \n",
       "1            1 ...         1    999         0         1           1.1   \n",
       "2            1 ...         1    999         0         1           1.1   \n",
       "3            1 ...         1    999         0         1           1.1   \n",
       "4            1 ...         1    999         0         1           1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0          93.994          -36.4      4.857       5191.0  0  \n",
       "1          93.994          -36.4      4.857       5191.0  0  \n",
       "2          93.994          -36.4      4.857       5191.0  0  \n",
       "3          93.994          -36.4      4.857       5191.0  0  \n",
       "4          93.994          -36.4      4.857       5191.0  0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we can view the first 5 encoded rows of the df.\n",
    "\n",
    "bank_additional_full_df = bank_additional_full_df.drop(['yHat'], axis=1)\n",
    "bank_additional_full_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.4 Explanatory Knowledge: Feature Correlation and Covariance \n",
    "\n",
    "Here we compare pairs of features, by finding the correlation between multiple features, depicted with a heat map for each for the Bank Additional Full dataset.\n",
    "\n",
    "**Question: Why do we want correlation instead of the covariance?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: Covariance and Correlation are two significantly used terms in the field of statistics and probability theory, where we can understand the difference between correlation and covariance matrices, and understand the application of the two in the field of analytics and datasets.\n",
    "\n",
    "As we see from the formula of covariance (biased), \n",
    "\n",
    "$$ Cov(X,Y) = \\frac{1}{n}\\sum({X}-{\\bar{X}})({Y}-{\\bar{Y}}) $$ \n",
    "\n",
    "it assumes the units from the product of the units of the two variables. This is because it divides the value of covariance by the product of standard deviations which have the same units. The value of covariance is affected by the change in scale of the variables. If all the values of the given variable are multiplied by a constant and all the values of another variable are multiplied, by a similar or different constant, then the value of covariance also changes.\n",
    "\n",
    "On the other hand, correlation is dimensionless i.e. **a unit-free measure of the relationship between variables** (which is what we need). However, the value of correlation is not influenced by the change in scale of the values. \n",
    "\n",
    "\n",
    "Lastly, another difference between covariance and correlation is the range of values that they can assume. While correlation coefficients lie between -1 and +1, covariance can take any value between $-\\infty$ and $+\\infty$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efe23191e10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can create a correlation matrix to grasp the correlation between features of the bank_additional_full_df \n",
    "# with the exception of the target variable (y)\n",
    "\n",
    "feats1 = bank_additional_full_df.iloc[:, :20]\n",
    "\n",
    "f,ax = plt.subplots(figsize=(25, 25))\n",
    "sns.heatmap(feats1.corr(), annot=True, linewidths=.5,ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the correlation matrix we see that the columns ['emp.var.rate', 'cons.price.idx', 'euribor3m', 'nr.employed'] are heavily correlated as seen by the color of their respective cells. Therefore, they can be dropped leaving features which have a correlation value less than or equal 0.28. \n",
    "\n",
    "Let's view the new correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efe21d33e48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can create a new correlation matrix to grasp the correlation between the remaining features\n",
    "# with the highly correlated features removed \n",
    "feats2 = bank_additional_full_df.drop(['emp.var.rate', 'cons.price.idx', 'euribor3m', 'nr.employed', 'y'], axis=1)\n",
    "\n",
    "g,ax = plt.subplots(figsize=(25, 25))\n",
    "sns.heatmap(feats2.corr(), annot=True, linewidths=.5,ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Experiments\n",
    "### 3.1 Predictive Modeling \n",
    "\n",
    "The task of predicting the value of an attribute by based on the value of other attributes:\n",
    "\n",
    "- The attributes that are used to make the prediction are known as the **explanatory attributes** (also known as independent variables or predictors) \n",
    "- The attribute to be predicted is known as the **taraget attribute** (also known as dependent variable, respond variable, or predictand)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We separate the df into the explanatory attributes (X) and taraget attribute (Y) \n",
    "X = bank_additional_full_df.drop(['emp.var.rate', 'cons.price.idx', 'euribor3m', 'nr.employed', 'y'], axis=1)\n",
    "Y = bank_additional_full_df.iloc[:,20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Gradient Boosting Classifier model\n",
    "\n",
    "In many supervised learning problems one has to output the variable $y$, using a training set ${\\{(x_1, y_1), ... (x_n, y_n)}\\}$ of known values for $x$ corresponding to values of $y$, the goal is to find an approximation $\\hat{F}(x)$ to a function $F(x)$ that minimizes the expected value of some specified lost function $L(y, F(x))$: \n",
    "\n",
    "$$ \\hat{F} = \\underset{F}{\\operatorname{argmin}} \\mathbf{E}_{x,y}[L(y, F(x))] $$\n",
    "\n",
    "Gradient boosting is an emsemble machine learning technique that builds the model in a stage-wise fashion as it allows optimization of an arbitrary differentiable loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for the Gradient Boosting Classifier model was 91.90%\n"
     ]
    }
   ],
   "source": [
    "# We further split the data into training and testing sets to train and test the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
    "gb = GradientBoostingClassifier(random_state = 42) \n",
    "gb_model = gb.fit(X_train, y_train)\n",
    "\n",
    "# Getting the score of feature matrix and its target values \n",
    "gb_model.score(X_test,y_test)\n",
    "\n",
    "print('The accuracy score for the Gradient Boosting Classifier model was %.2f%%' %(gb_model.score(X_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      0.97      0.96     14655\n",
      "          1       0.69      0.49      0.57      1821\n",
      "\n",
      "avg / total       0.91      0.92      0.91     16476\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      " [[14258   397]\n",
      " [  937   884]]\n"
     ]
    }
   ],
   "source": [
    "# Model Prediction \n",
    "gb_preds = gb_model.predict(X_test)\n",
    "\n",
    "# Print the classification report of the rf model\n",
    "print(classification_report(y_true = y_test, y_pred = gb_preds))\n",
    "\n",
    "#Print the confusion matrix of the rf model\n",
    "print('\\nConfusion Matrix:\\n', confusion_matrix(y_test,gb_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Random Forest Classifier model\n",
    "\n",
    "Random decision forests are another ensemble learning method for classification and regression by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for the Random Forest Classification model was 90.76%\n"
     ]
    }
   ],
   "source": [
    "# We further split the data into training and testing sets to train and test the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
    "rf = RandomForestClassifier(random_state = 42) \n",
    "rf_model = rf.fit(X_train, y_train)\n",
    "\n",
    "# Getting the score of feature matrix and its target values \n",
    "rf_model.score(X_test,y_test)\n",
    "\n",
    "print('The accuracy score for the Random Forest Classification model was %.2f%%' %(rf_model.score(X_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.97      0.95     14655\n",
      "          1       0.63      0.40      0.49      1821\n",
      "\n",
      "avg / total       0.90      0.91      0.90     16476\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      " [[14233   422]\n",
      " [ 1101   720]]\n"
     ]
    }
   ],
   "source": [
    "# Model Prediction \n",
    "rf_preds = rf_model.predict(X_test)\n",
    "\n",
    "# Print the classification report of the rf model\n",
    "print(classification_report(y_true = y_test, y_pred = rf_preds))\n",
    "\n",
    "#Print the confusion matrix of the rf model\n",
    "print('\\nConfusion Matrix:\\n', confusion_matrix(y_test,rf_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 MLP Classifier model \n",
    "\n",
    "A multilayer perceptron (MLP) is a class of feedforward artificial neural network. An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron, which can distinguish data that is not linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for the MLP Classification model was 90.97%\n"
     ]
    }
   ],
   "source": [
    "#We further split the data into training and testing sets to train and test the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
    "mlp = MLPClassifier(hidden_layer_sizes = (15,15,15), max_iter = 1000)\n",
    "mlp.fit(X_train,y_train)\n",
    "\n",
    "print('The accuracy score for the MLP Classification model was %.2f%%' %(mlp.score(X_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.97      0.95     14655\n",
      "          1       0.66      0.39      0.49      1821\n",
      "\n",
      "avg / total       0.90      0.91      0.90     16476\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      " [[14284   371]\n",
      " [ 1116   705]]\n"
     ]
    }
   ],
   "source": [
    "# Model Prediction \n",
    "mlp_preds = mlp.predict(X_test)\n",
    "\n",
    "# Print the classification report of the rf model\n",
    "print(classification_report(y_true = y_test, y_pred = mlp_preds))\n",
    "\n",
    "# Print the confusion matrix of the rf model\n",
    "print('\\nConfusion Matrix:\\n', confusion_matrix(y_test,mlp_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Logistic Regression model \n",
    "\n",
    "Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).  Like all regression analyses, the logistic regression is a predictive analysis.  Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for the Logistic regression model was 90.68%\n"
     ]
    }
   ],
   "source": [
    "# We further split the data into training and testing sets to train and test the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
    "lgr = LogisticRegression(random_state = 42) \n",
    "lgr_model = lgr.fit(X_train, y_train)\n",
    "\n",
    "# Getting the score of feature matrix and its target values \n",
    "lgr_model.score(X_test,y_test)\n",
    "\n",
    "print('The accuracy score for the Logistic regression model was %.2f%%' %(lgr_model.score(X_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.98      0.95     14655\n",
      "          1       0.65      0.35      0.45      1821\n",
      "\n",
      "avg / total       0.89      0.91      0.89     16476\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      " [[14306   349]\n",
      " [ 1186   635]]\n"
     ]
    }
   ],
   "source": [
    "# Model Prediction \n",
    "lgr_preds = lgr.predict(X_test)\n",
    "\n",
    "# Print the classification report of the rf model\n",
    "print(classification_report(y_true = y_test, y_pred = lgr_preds))\n",
    "\n",
    "# Print the confusion matrix of the rf model\n",
    "print('\\nConfusion Matrix:\\n', confusion_matrix(y_test,lgr_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Results\n",
    "\n",
    "### 4.1 Model Comparison\n",
    "\n",
    "In this subsection, 10 fold cross vaildation is being explored, as the dataset is large each model will have a different behavioral pattern result on each fold, which we will now examine to avoid overfitting and/ or underfitting. Overfitting is often a result of an excessively complicated model, and it can be prevented by fitting multiple models and using validation or cross-validation to compare their predictive accuracies on test data.  Intuitively, underfitting occurs when the model or the algorithm does not fit the data well enough. Underfitting is often a result of an excessively simple model, hence we will choose several models and report their mean accuracy and standard deviation. \n",
    "\n",
    "Cross-validation is a statistical method used to estimate the skill of machine learning models. It is commonly used in applied machine learning to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to implement, and results in skill estimates that generally have lower biases or a less optimistic estimate of the model skill than other methods, such as a simple train/test split. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " GB : 90.08% (0.098895)\n",
      " RF : 89.33% (0.104898)\n",
      " LDA : 90.21% (0.089433)\n",
      " KNN : 89.41% (0.095998)\n",
      " LGR : 89.42% (0.115526)\n",
      " SVM : 88.72% (0.121800)\n",
      " DT : 85.24% (0.126282)\n",
      " NB : 85.54% (0.127046)\n"
     ]
    }
   ],
   "source": [
    "# We now prepare our selected models\n",
    "models = []\n",
    "models.append(( ' GB ' , GradientBoostingClassifier()))\n",
    "models.append(( ' RF ' , RandomForestClassifier()))\n",
    "models.append(( ' LDA ' , LinearDiscriminantAnalysis()))\n",
    "models.append(( ' KNN ' , KNeighborsClassifier()))\n",
    "models.append(( ' LGR ' , LogisticRegression()))\n",
    "models.append(( ' SVM ' , SVC()))\n",
    "models.append(( ' DT ' , DecisionTreeClassifier()))\n",
    "models.append(( ' NB ' , GaussianNB()))\n",
    "\n",
    "# We then evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "scoring = 'accuracy'\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=42)\n",
    "    cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %.2f%% (%f)\" % (name, 100*cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Explanatory Knowledge: Model Information\n",
    "\n",
    "Here we can provide some information on the models used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(' GB ', GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "                learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                max_features=None, max_leaf_nodes=None,\n",
       "                min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                min_samples_leaf=1, min_samples_split=2,\n",
       "                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "                warm_start=False)),\n",
       " (' RF ',\n",
       "  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "              max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "              oob_score=False, random_state=None, verbose=0,\n",
       "              warm_start=False)),\n",
       " (' LDA ',\n",
       "  LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
       "                solver='svd', store_covariance=False, tol=0.0001)),\n",
       " (' KNN ',\n",
       "  KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "             metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "             weights='uniform')),\n",
       " (' LGR ',\n",
       "  LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "            intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "            penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "            verbose=0, warm_start=False)),\n",
       " (' SVM ', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)),\n",
       " (' DT ',\n",
       "  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "              splitter='best')),\n",
       " (' NB ', GaussianNB(priors=None))]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Model Behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Accuracy table below shows the results of the Model Comparison consisting of the **Mean Accuracy** and **Standard Deviation** of each model i.e. the square root of the average of the squared deviations from the mean.\n",
    "\n",
    "#### Table 1: Accuracy\n",
    "---\n",
    "\n",
    "| Model | Mean (%) | Std Deviation |\n",
    "|----------|-------------|\n",
    "| GB | 90.08% |(0.098827)\n",
    "|RF | 89.33% | (0.104898)\n",
    "| LDA | 90.21% |(0.089433)\n",
    "| KNN | 89.41% |(0.095998)\n",
    "| LGR | 89.42% |(0.115526)\n",
    "| SVM | 88.72%| (0.121800)\n",
    "| DT | 85.12% |(0.127163)\n",
    "| NB | 85.54% |(0.127046)\n",
    "\n",
    "---\n",
    "\n",
    "We can view the results of each fold of each model as named in the Accuracy table above respectively, in which we will create a Model CV results table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.96868172, 0.96358339, 0.96115562, 0.94270454, 0.94828842,\n",
       "        0.94828842, 0.89463462, 0.89342073, 0.86619718, 0.62141816]),\n",
       " array([0.96746783, 0.96528284, 0.95581452, 0.93226511, 0.94076232,\n",
       "        0.94149065, 0.88225297, 0.88832241, 0.86304031, 0.59616319]),\n",
       " array([0.96746783, 0.96649672, 0.96285506, 0.94294732, 0.95120175,\n",
       "        0.94561787, 0.89075018, 0.88492353, 0.84847013, 0.66002914]),\n",
       " array([0.95945618, 0.95630007, 0.95217286, 0.93542122, 0.93906288,\n",
       "        0.94270454, 0.88905074, 0.88662297, 0.85624089, 0.62384653]),\n",
       " array([0.96941005, 0.9667395 , 0.95994173, 0.94027677, 0.94974508,\n",
       "        0.94707453, 0.89633406, 0.89317796, 0.85454104, 0.56459446]),\n",
       " array([0.9720806 , 0.96479728, 0.95775674, 0.93372178, 0.93639233,\n",
       "        0.94488954, 0.8980335 , 0.8798252 , 0.8436134 , 0.54055367]),\n",
       " array([0.94950231, 0.95023064, 0.93663511, 0.91915513, 0.93299345,\n",
       "        0.92935178, 0.68366108, 0.82859917, 0.83681399, 0.55706654]),\n",
       " array([0.97038116, 0.96698228, 0.96067007, 0.94027677, 0.94828842,\n",
       "        0.94683176, 0.77518815, 0.6984705 , 0.71369597, 0.63331714])]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Table 2: Accuracy after Cross-Validation (%)\n",
    "\n",
    "---\n",
    "| Model | Fold 1 |Fold 2 |Fold 3 |Fold 4 |Fold 5 |Fold 6 |Fold 7 |Fold 8 |Fold 9 |Fold 10 |\n",
    "|------|------|------|------|------|------|------|------|------|\n",
    "| GB | **96.87**| 96.36| 96.12| 94.27| 94.83|94.83| 89.46| 89.34| 86.62| **62.21**\n",
    "| RF | **96.75**| 96.53| 95.58|93.23| 94.08| 94.15| 88.23| 88.83| 86.30|**59.62**\n",
    "| LDA | **96.75**| 96.65| 96.29| 94.30| 95.12|94.56|89.08|88.49|84.85|**66.00**\n",
    "| KNN | **95.95**|95.63|95.22|93.54|93.91|94.27|88.91|88.66|85.62|**62.38**\n",
    "| LGR | **96.94**|96.67 |96.00| 94.03|94.97|94.71|89.63|89.32|85.45|**56.46**\n",
    "|SVM |**97.21** |96.48|95.78|93.37|93.64|94.49|89.80|87.98|84.36|**54.06**\n",
    "| DT |95.00|**95.17**|93.59|91.43|93.37|92.96|66.23|83.03|83.95|**55.76**\n",
    "| NB |**97.04**| 96.70|96.07|94.03|94.83|94.68|77.52|69.85 |71.37|**63.33**\n",
    "\n",
    "---\n",
    "\n",
    "The accuracy percentages in bold represent the best and worst accuracies of the 10-fold cross validation of each model respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Visualization: Model comparison\n",
    "\n",
    "We now can view the performance of the model comparison with the lines on the exterior of the boxplot represent the highest and lowest accuracies respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot of the mean results of each supervised model for comparison \n",
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "fig.suptitle('Model Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Conclusion\n",
    "\n",
    "       \n",
    "        In previous works a dataset of a massive 150 features related with bank client, product and social-economic attributes were collected, however the data used in this project consisted of 41188 examples with 20 explanatory variables (predictor variables) with very little correlation, a target variable It can be said that alot of the original explanatory variables were previously removed for several reasons which may be high correlation, noise, and/ or useless in the prediction of the target variable.\n",
    "    \n",
    "        In this work, four highly correlated features were removed, further reducing the number of predictor variables as inputs to each model. This can be seen in the second correlation matrix as the highest correlated value is 0.28 i.e a tad larger that 25% pairwise correlation. When the models were trained on an 60/40 split the purpose of the individual model were to test the performance of each model on 40% of the data being trained on 60% of the data, namely 16476 examples where 14655 are 0's and 1821 are 1's. The classification report shows a representation of the main classification metrics on a per-class basis. This gives a deeper intuition of the classifier behavior over global accuracy which can mask functional weaknesses in one class of a multiclass problem. The metrics are defined in terms of true and false positives, and true and false negatives. Positive and negative in this case are generic names for the classes of a binary classification problem. As seen, the precision of the predicition of 1's were typically around 65%, this is a result of an inbalanced dataset since the number of 0's were 8 times as much as 1's. The Neural Network, i.e. Multi-Layer Perceptron consisted of 15 layers, thus the number of weights became increasing large as it was evident in the time complexity of the algorithm despite producing an accuracy score of 90.97%. Although, the Logistic Regression model produced the lowest accuracy score on the data, also with poor precision on prediciting 1's as it generated the largest number of true positives, compared to the other models.\n",
    "    \n",
    "        Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. In that, the 10-fold cross validation was applied where several machine learning algorithms were included. The models' behavior varied on each fold, as expected since each fold pertained to a new test set as seen in Table 2. The SVM model was computationally heavy as the input was over 41K examples and resulted in 88.72% accuracy mean. SVM did not perform well on the last fold, hence the decreasing of its accuracy, since employing the kernel trick does not scale well to a substantial amount of training samples, as it takes the form of a function that maps a single vector to a vector of higher dimensionality, thus approximating the kernel. Future work includes carrying out similar supervised ML techniques i.e the precision of 1's are much better. This can be done in several ways as two approaches to this task is undersampling the data to have a balanced dataset, and using the dataset available i.e the bank-additional.csv with 10% of the examples (4119), randomly selected from bank-additional-full.csv. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. References\n",
    "\n",
    "- [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014\n",
    "- http://archive.ics.uci.edu/ml/datasets/Bank+Marketing\n",
    "- https://scikit-learn.org/stable/supervised_learning.html#supervised-learning\n",
    "- https://en.wikipedia.org/wiki/Gradient_boosting\n",
    "- https://en.wikipedia.org/wiki/Random_forest\n",
    "- https://en.wikipedia.org/wiki/Multilayer_perceptron\n",
    "- https://www.statisticssolutions.com/what-is-logistic-regression/\n",
    "- https://en.wikipedia.org/wiki/Decision_tree_learning\n",
    "- https://scikit-learn.org/stable/modules/neighbors.html\n",
    "- https://scikit-learn.org/stable/modules/lda_qda.html\n",
    "- https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
    "- https://scikit-learn.org/stable/modules/svm.html#svc\n",
    "- https://scikit-learn.org/stable/modules/naive_bayes.html\n",
    "- https://www.scikit-yb.org/en/latest/api/classifier/classification_report.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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