Portuguse Bank_EnsembleTechnique.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Banking Domain\n",
    "        Banking industry is spending massive amount of money in the marketing strategy. This needs to be made more effective by understanding the customer needs and redesigning the prodcuts available. The given problem statement is mainly focused on the phone call marketing strategy. Often more than 1 contact to same client was required, in order to access if the product (term deposit) would be \"Yes\" or \"No\". This project might enable a target customer for future marketing plans. By analyzing the customer features, the bank will be able to predict customer behavior and identify which type of customer is more likely to make the term deposits in the portuguse bank. This result will help the bank to secure their deposits effectively and increase the satisfaction by reducing the advetisment to disqualified customers\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Data Cleansing\n",
    "\n",
    "Load  raw data-->Structure the data-->process the data-->Perform EDA-->Report the insights/graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is about the direct phone call marketing campaigns, which aim to promote term deposits among existing customers, by a Portuguese banking institution . The data is retrived from UCI Machine learning Repository : http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the necessary packages for finding the insights\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy.stats import ttest_ind,levene,shapiro,iqr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_color_codes=True\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "from pandas import read_csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the dataset file\n",
    "bank_data = pd.read_csv('bank-full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
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       "      <td>44</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-372</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>172</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>255</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>52</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>113</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>46</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-246</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>36</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>265</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45181</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>6879</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>nov</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>3</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45182</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>133</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>nov</td>\n",
       "      <td>401</td>\n",
       "      <td>2</td>\n",
       "      <td>187</td>\n",
       "      <td>5</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45183</th>\n",
       "      <td>70</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>324</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>nov</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>7</td>\n",
       "      <td>success</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45184</th>\n",
       "      <td>63</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1495</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>success</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45185</th>\n",
       "      <td>60</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>4256</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45186</th>\n",
       "      <td>59</td>\n",
       "      <td>unknown</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45187</th>\n",
       "      <td>32</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1168</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45188</th>\n",
       "      <td>29</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>703</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45189</th>\n",
       "      <td>25</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>199</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>173</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45190</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>136</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45191</th>\n",
       "      <td>75</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>3810</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45192</th>\n",
       "      <td>29</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>765</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45193</th>\n",
       "      <td>28</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>159</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>449</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45194</th>\n",
       "      <td>59</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>138</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>162</td>\n",
       "      <td>2</td>\n",
       "      <td>187</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45195</th>\n",
       "      <td>68</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1146</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>6</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45196</th>\n",
       "      <td>25</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>358</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45197</th>\n",
       "      <td>36</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1511</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45198</th>\n",
       "      <td>37</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1428</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>333</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45199</th>\n",
       "      <td>34</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1475</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>1166</td>\n",
       "      <td>3</td>\n",
       "      <td>530</td>\n",
       "      <td>12</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45200</th>\n",
       "      <td>38</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>557</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>1556</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45201</th>\n",
       "      <td>53</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>583</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45202</th>\n",
       "      <td>34</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>557</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45203</th>\n",
       "      <td>23</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>113</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>266</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45204</th>\n",
       "      <td>73</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2850</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45205</th>\n",
       "      <td>25</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>505</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>386</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>508</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>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age            job   marital  education default  balance housing loan  \\\n",
       "0       58     management   married   tertiary      no     2143     yes   no   \n",
       "1       44     technician    single  secondary      no       29     yes   no   \n",
       "2       33   entrepreneur   married  secondary      no        2     yes  yes   \n",
       "3       47    blue-collar   married    unknown      no     1506     yes   no   \n",
       "4       33        unknown    single    unknown      no        1      no   no   \n",
       "5       35     management   married   tertiary      no      231     yes   no   \n",
       "6       28     management    single   tertiary      no      447     yes  yes   \n",
       "7       42   entrepreneur  divorced   tertiary     yes        2     yes   no   \n",
       "8       58        retired   married    primary      no      121     yes   no   \n",
       "9       43     technician    single  secondary      no      593     yes   no   \n",
       "10      41         admin.  divorced  secondary      no      270     yes   no   \n",
       "11      29         admin.    single  secondary      no      390     yes   no   \n",
       "12      53     technician   married  secondary      no        6     yes   no   \n",
       "13      58     technician   married    unknown      no       71     yes   no   \n",
       "14      57       services   married  secondary      no      162     yes   no   \n",
       "15      51        retired   married    primary      no      229     yes   no   \n",
       "16      45         admin.    single    unknown      no       13     yes   no   \n",
       "17      57    blue-collar   married    primary      no       52     yes   no   \n",
       "18      60        retired   married    primary      no       60     yes   no   \n",
       "19      33       services   married  secondary      no        0     yes   no   \n",
       "20      28    blue-collar   married  secondary      no      723     yes  yes   \n",
       "21      56     management   married   tertiary      no      779     yes   no   \n",
       "22      32    blue-collar    single    primary      no       23     yes  yes   \n",
       "23      25       services   married  secondary      no       50     yes   no   \n",
       "24      40        retired   married    primary      no        0     yes  yes   \n",
       "25      44         admin.   married  secondary      no     -372     yes   no   \n",
       "26      39     management    single   tertiary      no      255     yes   no   \n",
       "27      52   entrepreneur   married  secondary      no      113     yes  yes   \n",
       "28      46     management    single  secondary      no     -246     yes   no   \n",
       "29      36     technician    single  secondary      no      265     yes  yes   \n",
       "...    ...            ...       ...        ...     ...      ...     ...  ...   \n",
       "45181   46    blue-collar   married  secondary      no     6879      no   no   \n",
       "45182   34     technician   married  secondary      no      133      no   no   \n",
       "45183   70        retired   married    primary      no      324      no   no   \n",
       "45184   63        retired   married  secondary      no     1495      no   no   \n",
       "45185   60       services   married   tertiary      no     4256     yes   no   \n",
       "45186   59        unknown   married    unknown      no     1500      no   no   \n",
       "45187   32       services    single  secondary      no     1168     yes   no   \n",
       "45188   29     management    single  secondary      no      703     yes   no   \n",
       "45189   25       services    single  secondary      no      199      no   no   \n",
       "45190   32    blue-collar   married  secondary      no      136      no   no   \n",
       "45191   75        retired  divorced   tertiary      no     3810     yes   no   \n",
       "45192   29     management    single   tertiary      no      765      no   no   \n",
       "45193   28  self-employed    single   tertiary      no      159      no   no   \n",
       "45194   59     management   married   tertiary      no      138     yes  yes   \n",
       "45195   68        retired   married  secondary      no     1146      no   no   \n",
       "45196   25        student    single  secondary      no      358      no   no   \n",
       "45197   36     management    single  secondary      no     1511     yes   no   \n",
       "45198   37     management   married   tertiary      no     1428      no   no   \n",
       "45199   34    blue-collar    single  secondary      no     1475     yes   no   \n",
       "45200   38     technician   married  secondary      no      557     yes   no   \n",
       "45201   53     management   married   tertiary      no      583      no   no   \n",
       "45202   34         admin.    single  secondary      no      557      no   no   \n",
       "45203   23        student    single   tertiary      no      113      no   no   \n",
       "45204   73        retired   married  secondary      no     2850      no   no   \n",
       "45205   25     technician    single  secondary      no      505      no  yes   \n",
       "45206   51     technician   married   tertiary      no      825      no   no   \n",
       "45207   71        retired  divorced    primary      no     1729      no   no   \n",
       "45208   72        retired   married  secondary      no     5715      no   no   \n",
       "45209   57    blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37   entrepreneur   married  secondary      no     2971      no   no   \n",
       "\n",
       "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "0        unknown    5   may       261         1     -1         0  unknown   \n",
       "1        unknown    5   may       151         1     -1         0  unknown   \n",
       "2        unknown    5   may        76         1     -1         0  unknown   \n",
       "3        unknown    5   may        92         1     -1         0  unknown   \n",
       "4        unknown    5   may       198         1     -1         0  unknown   \n",
       "5        unknown    5   may       139         1     -1         0  unknown   \n",
       "6        unknown    5   may       217         1     -1         0  unknown   \n",
       "7        unknown    5   may       380         1     -1         0  unknown   \n",
       "8        unknown    5   may        50         1     -1         0  unknown   \n",
       "9        unknown    5   may        55         1     -1         0  unknown   \n",
       "10       unknown    5   may       222         1     -1         0  unknown   \n",
       "11       unknown    5   may       137         1     -1         0  unknown   \n",
       "12       unknown    5   may       517         1     -1         0  unknown   \n",
       "13       unknown    5   may        71         1     -1         0  unknown   \n",
       "14       unknown    5   may       174         1     -1         0  unknown   \n",
       "15       unknown    5   may       353         1     -1         0  unknown   \n",
       "16       unknown    5   may        98         1     -1         0  unknown   \n",
       "17       unknown    5   may        38         1     -1         0  unknown   \n",
       "18       unknown    5   may       219         1     -1         0  unknown   \n",
       "19       unknown    5   may        54         1     -1         0  unknown   \n",
       "20       unknown    5   may       262         1     -1         0  unknown   \n",
       "21       unknown    5   may       164         1     -1         0  unknown   \n",
       "22       unknown    5   may       160         1     -1         0  unknown   \n",
       "23       unknown    5   may       342         1     -1         0  unknown   \n",
       "24       unknown    5   may       181         1     -1         0  unknown   \n",
       "25       unknown    5   may       172         1     -1         0  unknown   \n",
       "26       unknown    5   may       296         1     -1         0  unknown   \n",
       "27       unknown    5   may       127         1     -1         0  unknown   \n",
       "28       unknown    5   may       255         2     -1         0  unknown   \n",
       "29       unknown    5   may       348         1     -1         0  unknown   \n",
       "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
       "45181   cellular   15   nov        74         2    118         3  failure   \n",
       "45182   cellular   15   nov       401         2    187         5  success   \n",
       "45183   cellular   15   nov        78         1     96         7  success   \n",
       "45184   cellular   16   nov       138         1     22         5  success   \n",
       "45185   cellular   16   nov       200         1     92         4  success   \n",
       "45186   cellular   16   nov       280         1    104         2  failure   \n",
       "45187   cellular   16   nov       411         1     -1         0  unknown   \n",
       "45188   cellular   16   nov       236         1    550         2  success   \n",
       "45189   cellular   16   nov       173         1     92         5  failure   \n",
       "45190   cellular   16   nov       206         1    188         3  success   \n",
       "45191   cellular   16   nov       262         1    183         1  failure   \n",
       "45192   cellular   16   nov       238         1     -1         0  unknown   \n",
       "45193   cellular   16   nov       449         2     33         4  success   \n",
       "45194   cellular   16   nov       162         2    187         5  failure   \n",
       "45195   cellular   16   nov       212         1    187         6  success   \n",
       "45196   cellular   16   nov       330         1     -1         0  unknown   \n",
       "45197   cellular   16   nov       270         1     -1         0  unknown   \n",
       "45198   cellular   16   nov       333         2     -1         0  unknown   \n",
       "45199   cellular   16   nov      1166         3    530        12    other   \n",
       "45200   cellular   16   nov      1556         4     -1         0  unknown   \n",
       "45201   cellular   17   nov       226         1    184         4  success   \n",
       "45202   cellular   17   nov       224         1     -1         0  unknown   \n",
       "45203   cellular   17   nov       266         1     -1         0  unknown   \n",
       "45204   cellular   17   nov       300         1     40         8  failure   \n",
       "45205   cellular   17   nov       386         2     -1         0  unknown   \n",
       "45206   cellular   17   nov       977         3     -1         0  unknown   \n",
       "45207   cellular   17   nov       456         2     -1         0  unknown   \n",
       "45208   cellular   17   nov      1127         5    184         3  success   \n",
       "45209  telephone   17   nov       508         4     -1         0  unknown   \n",
       "45210   cellular   17   nov       361         2    188        11    other   \n",
       "\n",
       "      Target  \n",
       "0         no  \n",
       "1         no  \n",
       "2         no  \n",
       "3         no  \n",
       "4         no  \n",
       "5         no  \n",
       "6         no  \n",
       "7         no  \n",
       "8         no  \n",
       "9         no  \n",
       "10        no  \n",
       "11        no  \n",
       "12        no  \n",
       "13        no  \n",
       "14        no  \n",
       "15        no  \n",
       "16        no  \n",
       "17        no  \n",
       "18        no  \n",
       "19        no  \n",
       "20        no  \n",
       "21        no  \n",
       "22        no  \n",
       "23        no  \n",
       "24        no  \n",
       "25        no  \n",
       "26        no  \n",
       "27        no  \n",
       "28        no  \n",
       "29        no  \n",
       "...      ...  \n",
       "45181     no  \n",
       "45182    yes  \n",
       "45183     no  \n",
       "45184     no  \n",
       "45185    yes  \n",
       "45186     no  \n",
       "45187    yes  \n",
       "45188    yes  \n",
       "45189     no  \n",
       "45190    yes  \n",
       "45191    yes  \n",
       "45192    yes  \n",
       "45193    yes  \n",
       "45194     no  \n",
       "45195    yes  \n",
       "45196    yes  \n",
       "45197    yes  \n",
       "45198     no  \n",
       "45199     no  \n",
       "45200    yes  \n",
       "45201    yes  \n",
       "45202    yes  \n",
       "45203    yes  \n",
       "45204    yes  \n",
       "45205    yes  \n",
       "45206    yes  \n",
       "45207    yes  \n",
       "45208    yes  \n",
       "45209     no  \n",
       "45210     no  \n",
       "\n",
       "[45211 rows x 17 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data\n",
    "#There are around 45.2k customer records in the given data set\n",
    "#There 17 variables out of which 16 are input and 1 is the target variable with yes/no value\n",
    "#Most of the variables are categorical variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Target       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To verify the datatypes of the all the attributes in the dataset\n",
    "bank_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.describe()\n",
    "\n",
    "#The mean balance is way much less than the max balance value. This shows there might be outlier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>40.936210</td>\n",
       "      <td>10.618762</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>-8019.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>102127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>4918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>45211.0</td>\n",
       "      <td>0.580323</td>\n",
       "      <td>2.303441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std     min    25%    50%     75%  \\\n",
       "age       45211.0    40.936210    10.618762    18.0   33.0   39.0    48.0   \n",
       "balance   45211.0  1362.272058  3044.765829 -8019.0   72.0  448.0  1428.0   \n",
       "day       45211.0    15.806419     8.322476     1.0    8.0   16.0    21.0   \n",
       "duration  45211.0   258.163080   257.527812     0.0  103.0  180.0   319.0   \n",
       "campaign  45211.0     2.763841     3.098021     1.0    1.0    2.0     3.0   \n",
       "pdays     45211.0    40.197828   100.128746    -1.0   -1.0   -1.0    -1.0   \n",
       "previous  45211.0     0.580323     2.303441     0.0    0.0    0.0     0.0   \n",
       "\n",
       "               max  \n",
       "age           95.0  \n",
       "balance   102127.0  \n",
       "day           31.0  \n",
       "duration    4918.0  \n",
       "campaign      63.0  \n",
       "pdays        871.0  \n",
       "previous     275.0  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get the behavior of global statistical functions using describe\n",
    "bank_data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.dropna of        age            job   marital  education default  balance housing loan  \\\n",
       "0       58     management   married   tertiary      no     2143     yes   no   \n",
       "1       44     technician    single  secondary      no       29     yes   no   \n",
       "2       33   entrepreneur   married  secondary      no        2     yes  yes   \n",
       "3       47    blue-collar   married    unknown      no     1506     yes   no   \n",
       "4       33        unknown    single    unknown      no        1      no   no   \n",
       "5       35     management   married   tertiary      no      231     yes   no   \n",
       "6       28     management    single   tertiary      no      447     yes  yes   \n",
       "7       42   entrepreneur  divorced   tertiary     yes        2     yes   no   \n",
       "8       58        retired   married    primary      no      121     yes   no   \n",
       "9       43     technician    single  secondary      no      593     yes   no   \n",
       "10      41         admin.  divorced  secondary      no      270     yes   no   \n",
       "11      29         admin.    single  secondary      no      390     yes   no   \n",
       "12      53     technician   married  secondary      no        6     yes   no   \n",
       "13      58     technician   married    unknown      no       71     yes   no   \n",
       "14      57       services   married  secondary      no      162     yes   no   \n",
       "15      51        retired   married    primary      no      229     yes   no   \n",
       "16      45         admin.    single    unknown      no       13     yes   no   \n",
       "17      57    blue-collar   married    primary      no       52     yes   no   \n",
       "18      60        retired   married    primary      no       60     yes   no   \n",
       "19      33       services   married  secondary      no        0     yes   no   \n",
       "20      28    blue-collar   married  secondary      no      723     yes  yes   \n",
       "21      56     management   married   tertiary      no      779     yes   no   \n",
       "22      32    blue-collar    single    primary      no       23     yes  yes   \n",
       "23      25       services   married  secondary      no       50     yes   no   \n",
       "24      40        retired   married    primary      no        0     yes  yes   \n",
       "25      44         admin.   married  secondary      no     -372     yes   no   \n",
       "26      39     management    single   tertiary      no      255     yes   no   \n",
       "27      52   entrepreneur   married  secondary      no      113     yes  yes   \n",
       "28      46     management    single  secondary      no     -246     yes   no   \n",
       "29      36     technician    single  secondary      no      265     yes  yes   \n",
       "...    ...            ...       ...        ...     ...      ...     ...  ...   \n",
       "45181   46    blue-collar   married  secondary      no     6879      no   no   \n",
       "45182   34     technician   married  secondary      no      133      no   no   \n",
       "45183   70        retired   married    primary      no      324      no   no   \n",
       "45184   63        retired   married  secondary      no     1495      no   no   \n",
       "45185   60       services   married   tertiary      no     4256     yes   no   \n",
       "45186   59        unknown   married    unknown      no     1500      no   no   \n",
       "45187   32       services    single  secondary      no     1168     yes   no   \n",
       "45188   29     management    single  secondary      no      703     yes   no   \n",
       "45189   25       services    single  secondary      no      199      no   no   \n",
       "45190   32    blue-collar   married  secondary      no      136      no   no   \n",
       "45191   75        retired  divorced   tertiary      no     3810     yes   no   \n",
       "45192   29     management    single   tertiary      no      765      no   no   \n",
       "45193   28  self-employed    single   tertiary      no      159      no   no   \n",
       "45194   59     management   married   tertiary      no      138     yes  yes   \n",
       "45195   68        retired   married  secondary      no     1146      no   no   \n",
       "45196   25        student    single  secondary      no      358      no   no   \n",
       "45197   36     management    single  secondary      no     1511     yes   no   \n",
       "45198   37     management   married   tertiary      no     1428      no   no   \n",
       "45199   34    blue-collar    single  secondary      no     1475     yes   no   \n",
       "45200   38     technician   married  secondary      no      557     yes   no   \n",
       "45201   53     management   married   tertiary      no      583      no   no   \n",
       "45202   34         admin.    single  secondary      no      557      no   no   \n",
       "45203   23        student    single   tertiary      no      113      no   no   \n",
       "45204   73        retired   married  secondary      no     2850      no   no   \n",
       "45205   25     technician    single  secondary      no      505      no  yes   \n",
       "45206   51     technician   married   tertiary      no      825      no   no   \n",
       "45207   71        retired  divorced    primary      no     1729      no   no   \n",
       "45208   72        retired   married  secondary      no     5715      no   no   \n",
       "45209   57    blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37   entrepreneur   married  secondary      no     2971      no   no   \n",
       "\n",
       "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "0        unknown    5   may       261         1     -1         0  unknown   \n",
       "1        unknown    5   may       151         1     -1         0  unknown   \n",
       "2        unknown    5   may        76         1     -1         0  unknown   \n",
       "3        unknown    5   may        92         1     -1         0  unknown   \n",
       "4        unknown    5   may       198         1     -1         0  unknown   \n",
       "5        unknown    5   may       139         1     -1         0  unknown   \n",
       "6        unknown    5   may       217         1     -1         0  unknown   \n",
       "7        unknown    5   may       380         1     -1         0  unknown   \n",
       "8        unknown    5   may        50         1     -1         0  unknown   \n",
       "9        unknown    5   may        55         1     -1         0  unknown   \n",
       "10       unknown    5   may       222         1     -1         0  unknown   \n",
       "11       unknown    5   may       137         1     -1         0  unknown   \n",
       "12       unknown    5   may       517         1     -1         0  unknown   \n",
       "13       unknown    5   may        71         1     -1         0  unknown   \n",
       "14       unknown    5   may       174         1     -1         0  unknown   \n",
       "15       unknown    5   may       353         1     -1         0  unknown   \n",
       "16       unknown    5   may        98         1     -1         0  unknown   \n",
       "17       unknown    5   may        38         1     -1         0  unknown   \n",
       "18       unknown    5   may       219         1     -1         0  unknown   \n",
       "19       unknown    5   may        54         1     -1         0  unknown   \n",
       "20       unknown    5   may       262         1     -1         0  unknown   \n",
       "21       unknown    5   may       164         1     -1         0  unknown   \n",
       "22       unknown    5   may       160         1     -1         0  unknown   \n",
       "23       unknown    5   may       342         1     -1         0  unknown   \n",
       "24       unknown    5   may       181         1     -1         0  unknown   \n",
       "25       unknown    5   may       172         1     -1         0  unknown   \n",
       "26       unknown    5   may       296         1     -1         0  unknown   \n",
       "27       unknown    5   may       127         1     -1         0  unknown   \n",
       "28       unknown    5   may       255         2     -1         0  unknown   \n",
       "29       unknown    5   may       348         1     -1         0  unknown   \n",
       "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
       "45181   cellular   15   nov        74         2    118         3  failure   \n",
       "45182   cellular   15   nov       401         2    187         5  success   \n",
       "45183   cellular   15   nov        78         1     96         7  success   \n",
       "45184   cellular   16   nov       138         1     22         5  success   \n",
       "45185   cellular   16   nov       200         1     92         4  success   \n",
       "45186   cellular   16   nov       280         1    104         2  failure   \n",
       "45187   cellular   16   nov       411         1     -1         0  unknown   \n",
       "45188   cellular   16   nov       236         1    550         2  success   \n",
       "45189   cellular   16   nov       173         1     92         5  failure   \n",
       "45190   cellular   16   nov       206         1    188         3  success   \n",
       "45191   cellular   16   nov       262         1    183         1  failure   \n",
       "45192   cellular   16   nov       238         1     -1         0  unknown   \n",
       "45193   cellular   16   nov       449         2     33         4  success   \n",
       "45194   cellular   16   nov       162         2    187         5  failure   \n",
       "45195   cellular   16   nov       212         1    187         6  success   \n",
       "45196   cellular   16   nov       330         1     -1         0  unknown   \n",
       "45197   cellular   16   nov       270         1     -1         0  unknown   \n",
       "45198   cellular   16   nov       333         2     -1         0  unknown   \n",
       "45199   cellular   16   nov      1166         3    530        12    other   \n",
       "45200   cellular   16   nov      1556         4     -1         0  unknown   \n",
       "45201   cellular   17   nov       226         1    184         4  success   \n",
       "45202   cellular   17   nov       224         1     -1         0  unknown   \n",
       "45203   cellular   17   nov       266         1     -1         0  unknown   \n",
       "45204   cellular   17   nov       300         1     40         8  failure   \n",
       "45205   cellular   17   nov       386         2     -1         0  unknown   \n",
       "45206   cellular   17   nov       977         3     -1         0  unknown   \n",
       "45207   cellular   17   nov       456         2     -1         0  unknown   \n",
       "45208   cellular   17   nov      1127         5    184         3  success   \n",
       "45209  telephone   17   nov       508         4     -1         0  unknown   \n",
       "45210   cellular   17   nov       361         2    188        11    other   \n",
       "\n",
       "      Target  \n",
       "0         no  \n",
       "1         no  \n",
       "2         no  \n",
       "3         no  \n",
       "4         no  \n",
       "5         no  \n",
       "6         no  \n",
       "7         no  \n",
       "8         no  \n",
       "9         no  \n",
       "10        no  \n",
       "11        no  \n",
       "12        no  \n",
       "13        no  \n",
       "14        no  \n",
       "15        no  \n",
       "16        no  \n",
       "17        no  \n",
       "18        no  \n",
       "19        no  \n",
       "20        no  \n",
       "21        no  \n",
       "22        no  \n",
       "23        no  \n",
       "24        no  \n",
       "25        no  \n",
       "26        no  \n",
       "27        no  \n",
       "28        no  \n",
       "29        no  \n",
       "...      ...  \n",
       "45181     no  \n",
       "45182    yes  \n",
       "45183     no  \n",
       "45184     no  \n",
       "45185    yes  \n",
       "45186     no  \n",
       "45187    yes  \n",
       "45188    yes  \n",
       "45189     no  \n",
       "45190    yes  \n",
       "45191    yes  \n",
       "45192    yes  \n",
       "45193    yes  \n",
       "45194     no  \n",
       "45195    yes  \n",
       "45196    yes  \n",
       "45197    yes  \n",
       "45198     no  \n",
       "45199     no  \n",
       "45200    yes  \n",
       "45201    yes  \n",
       "45202    yes  \n",
       "45203    yes  \n",
       "45204    yes  \n",
       "45205    yes  \n",
       "45206    yes  \n",
       "45207    yes  \n",
       "45208    yes  \n",
       "45209     no  \n",
       "45210     no  \n",
       "\n",
       "[45211 rows x 17 columns]>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.dropna\n",
    "#There are no null values in the dataset but there are some \"unknown\" values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the 'unknown' to 'other'\n",
    "bank_data[['job','education','contact']] = bank_data[['job','education','contact']].replace(['unknown'],'other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</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>33</td>\n",
       "      <td>other</td>\n",
       "      <td>single</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>231</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>447</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>121</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>593</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>222</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>390</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>6</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>71</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>162</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>51</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>229</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>353</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>13</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>52</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>60</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>219</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>33</td>\n",
       "      <td>services</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>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>723</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>56</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>779</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>23</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>50</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>44</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-372</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>172</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>255</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>52</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>113</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>46</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-246</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>36</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>265</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>other</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45181</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>6879</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>nov</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>3</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45182</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>133</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>nov</td>\n",
       "      <td>401</td>\n",
       "      <td>2</td>\n",
       "      <td>187</td>\n",
       "      <td>5</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45183</th>\n",
       "      <td>70</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>324</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>nov</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>7</td>\n",
       "      <td>success</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45184</th>\n",
       "      <td>63</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1495</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>success</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45185</th>\n",
       "      <td>60</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>4256</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45186</th>\n",
       "      <td>59</td>\n",
       "      <td>other</td>\n",
       "      <td>married</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>1500</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45187</th>\n",
       "      <td>32</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1168</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45188</th>\n",
       "      <td>29</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>703</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45189</th>\n",
       "      <td>25</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>199</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>173</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45190</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>136</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>206</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45191</th>\n",
       "      <td>75</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>3810</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45192</th>\n",
       "      <td>29</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>765</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45193</th>\n",
       "      <td>28</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>159</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>449</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45194</th>\n",
       "      <td>59</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>138</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>162</td>\n",
       "      <td>2</td>\n",
       "      <td>187</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45195</th>\n",
       "      <td>68</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1146</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>6</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45196</th>\n",
       "      <td>25</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>358</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45197</th>\n",
       "      <td>36</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1511</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45198</th>\n",
       "      <td>37</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1428</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>333</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45199</th>\n",
       "      <td>34</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1475</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>1166</td>\n",
       "      <td>3</td>\n",
       "      <td>530</td>\n",
       "      <td>12</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45200</th>\n",
       "      <td>38</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>557</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>nov</td>\n",
       "      <td>1556</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45201</th>\n",
       "      <td>53</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>583</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45202</th>\n",
       "      <td>34</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>557</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45203</th>\n",
       "      <td>23</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>113</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>266</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45204</th>\n",
       "      <td>73</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2850</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45205</th>\n",
       "      <td>25</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>505</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>386</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>508</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>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age            job   marital  education default  balance housing loan  \\\n",
       "0       58     management   married   tertiary      no     2143     yes   no   \n",
       "1       44     technician    single  secondary      no       29     yes   no   \n",
       "2       33   entrepreneur   married  secondary      no        2     yes  yes   \n",
       "3       47    blue-collar   married      other      no     1506     yes   no   \n",
       "4       33          other    single      other      no        1      no   no   \n",
       "5       35     management   married   tertiary      no      231     yes   no   \n",
       "6       28     management    single   tertiary      no      447     yes  yes   \n",
       "7       42   entrepreneur  divorced   tertiary     yes        2     yes   no   \n",
       "8       58        retired   married    primary      no      121     yes   no   \n",
       "9       43     technician    single  secondary      no      593     yes   no   \n",
       "10      41         admin.  divorced  secondary      no      270     yes   no   \n",
       "11      29         admin.    single  secondary      no      390     yes   no   \n",
       "12      53     technician   married  secondary      no        6     yes   no   \n",
       "13      58     technician   married      other      no       71     yes   no   \n",
       "14      57       services   married  secondary      no      162     yes   no   \n",
       "15      51        retired   married    primary      no      229     yes   no   \n",
       "16      45         admin.    single      other      no       13     yes   no   \n",
       "17      57    blue-collar   married    primary      no       52     yes   no   \n",
       "18      60        retired   married    primary      no       60     yes   no   \n",
       "19      33       services   married  secondary      no        0     yes   no   \n",
       "20      28    blue-collar   married  secondary      no      723     yes  yes   \n",
       "21      56     management   married   tertiary      no      779     yes   no   \n",
       "22      32    blue-collar    single    primary      no       23     yes  yes   \n",
       "23      25       services   married  secondary      no       50     yes   no   \n",
       "24      40        retired   married    primary      no        0     yes  yes   \n",
       "25      44         admin.   married  secondary      no     -372     yes   no   \n",
       "26      39     management    single   tertiary      no      255     yes   no   \n",
       "27      52   entrepreneur   married  secondary      no      113     yes  yes   \n",
       "28      46     management    single  secondary      no     -246     yes   no   \n",
       "29      36     technician    single  secondary      no      265     yes  yes   \n",
       "...    ...            ...       ...        ...     ...      ...     ...  ...   \n",
       "45181   46    blue-collar   married  secondary      no     6879      no   no   \n",
       "45182   34     technician   married  secondary      no      133      no   no   \n",
       "45183   70        retired   married    primary      no      324      no   no   \n",
       "45184   63        retired   married  secondary      no     1495      no   no   \n",
       "45185   60       services   married   tertiary      no     4256     yes   no   \n",
       "45186   59          other   married      other      no     1500      no   no   \n",
       "45187   32       services    single  secondary      no     1168     yes   no   \n",
       "45188   29     management    single  secondary      no      703     yes   no   \n",
       "45189   25       services    single  secondary      no      199      no   no   \n",
       "45190   32    blue-collar   married  secondary      no      136      no   no   \n",
       "45191   75        retired  divorced   tertiary      no     3810     yes   no   \n",
       "45192   29     management    single   tertiary      no      765      no   no   \n",
       "45193   28  self-employed    single   tertiary      no      159      no   no   \n",
       "45194   59     management   married   tertiary      no      138     yes  yes   \n",
       "45195   68        retired   married  secondary      no     1146      no   no   \n",
       "45196   25        student    single  secondary      no      358      no   no   \n",
       "45197   36     management    single  secondary      no     1511     yes   no   \n",
       "45198   37     management   married   tertiary      no     1428      no   no   \n",
       "45199   34    blue-collar    single  secondary      no     1475     yes   no   \n",
       "45200   38     technician   married  secondary      no      557     yes   no   \n",
       "45201   53     management   married   tertiary      no      583      no   no   \n",
       "45202   34         admin.    single  secondary      no      557      no   no   \n",
       "45203   23        student    single   tertiary      no      113      no   no   \n",
       "45204   73        retired   married  secondary      no     2850      no   no   \n",
       "45205   25     technician    single  secondary      no      505      no  yes   \n",
       "45206   51     technician   married   tertiary      no      825      no   no   \n",
       "45207   71        retired  divorced    primary      no     1729      no   no   \n",
       "45208   72        retired   married  secondary      no     5715      no   no   \n",
       "45209   57    blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37   entrepreneur   married  secondary      no     2971      no   no   \n",
       "\n",
       "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "0          other    5   may       261         1     -1         0  unknown   \n",
       "1          other    5   may       151         1     -1         0  unknown   \n",
       "2          other    5   may        76         1     -1         0  unknown   \n",
       "3          other    5   may        92         1     -1         0  unknown   \n",
       "4          other    5   may       198         1     -1         0  unknown   \n",
       "5          other    5   may       139         1     -1         0  unknown   \n",
       "6          other    5   may       217         1     -1         0  unknown   \n",
       "7          other    5   may       380         1     -1         0  unknown   \n",
       "8          other    5   may        50         1     -1         0  unknown   \n",
       "9          other    5   may        55         1     -1         0  unknown   \n",
       "10         other    5   may       222         1     -1         0  unknown   \n",
       "11         other    5   may       137         1     -1         0  unknown   \n",
       "12         other    5   may       517         1     -1         0  unknown   \n",
       "13         other    5   may        71         1     -1         0  unknown   \n",
       "14         other    5   may       174         1     -1         0  unknown   \n",
       "15         other    5   may       353         1     -1         0  unknown   \n",
       "16         other    5   may        98         1     -1         0  unknown   \n",
       "17         other    5   may        38         1     -1         0  unknown   \n",
       "18         other    5   may       219         1     -1         0  unknown   \n",
       "19         other    5   may        54         1     -1         0  unknown   \n",
       "20         other    5   may       262         1     -1         0  unknown   \n",
       "21         other    5   may       164         1     -1         0  unknown   \n",
       "22         other    5   may       160         1     -1         0  unknown   \n",
       "23         other    5   may       342         1     -1         0  unknown   \n",
       "24         other    5   may       181         1     -1         0  unknown   \n",
       "25         other    5   may       172         1     -1         0  unknown   \n",
       "26         other    5   may       296         1     -1         0  unknown   \n",
       "27         other    5   may       127         1     -1         0  unknown   \n",
       "28         other    5   may       255         2     -1         0  unknown   \n",
       "29         other    5   may       348         1     -1         0  unknown   \n",
       "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
       "45181   cellular   15   nov        74         2    118         3  failure   \n",
       "45182   cellular   15   nov       401         2    187         5  success   \n",
       "45183   cellular   15   nov        78         1     96         7  success   \n",
       "45184   cellular   16   nov       138         1     22         5  success   \n",
       "45185   cellular   16   nov       200         1     92         4  success   \n",
       "45186   cellular   16   nov       280         1    104         2  failure   \n",
       "45187   cellular   16   nov       411         1     -1         0  unknown   \n",
       "45188   cellular   16   nov       236         1    550         2  success   \n",
       "45189   cellular   16   nov       173         1     92         5  failure   \n",
       "45190   cellular   16   nov       206         1    188         3  success   \n",
       "45191   cellular   16   nov       262         1    183         1  failure   \n",
       "45192   cellular   16   nov       238         1     -1         0  unknown   \n",
       "45193   cellular   16   nov       449         2     33         4  success   \n",
       "45194   cellular   16   nov       162         2    187         5  failure   \n",
       "45195   cellular   16   nov       212         1    187         6  success   \n",
       "45196   cellular   16   nov       330         1     -1         0  unknown   \n",
       "45197   cellular   16   nov       270         1     -1         0  unknown   \n",
       "45198   cellular   16   nov       333         2     -1         0  unknown   \n",
       "45199   cellular   16   nov      1166         3    530        12    other   \n",
       "45200   cellular   16   nov      1556         4     -1         0  unknown   \n",
       "45201   cellular   17   nov       226         1    184         4  success   \n",
       "45202   cellular   17   nov       224         1     -1         0  unknown   \n",
       "45203   cellular   17   nov       266         1     -1         0  unknown   \n",
       "45204   cellular   17   nov       300         1     40         8  failure   \n",
       "45205   cellular   17   nov       386         2     -1         0  unknown   \n",
       "45206   cellular   17   nov       977         3     -1         0  unknown   \n",
       "45207   cellular   17   nov       456         2     -1         0  unknown   \n",
       "45208   cellular   17   nov      1127         5    184         3  success   \n",
       "45209  telephone   17   nov       508         4     -1         0  unknown   \n",
       "45210   cellular   17   nov       361         2    188        11    other   \n",
       "\n",
       "      Target  \n",
       "0         no  \n",
       "1         no  \n",
       "2         no  \n",
       "3         no  \n",
       "4         no  \n",
       "5         no  \n",
       "6         no  \n",
       "7         no  \n",
       "8         no  \n",
       "9         no  \n",
       "10        no  \n",
       "11        no  \n",
       "12        no  \n",
       "13        no  \n",
       "14        no  \n",
       "15        no  \n",
       "16        no  \n",
       "17        no  \n",
       "18        no  \n",
       "19        no  \n",
       "20        no  \n",
       "21        no  \n",
       "22        no  \n",
       "23        no  \n",
       "24        no  \n",
       "25        no  \n",
       "26        no  \n",
       "27        no  \n",
       "28        no  \n",
       "29        no  \n",
       "...      ...  \n",
       "45181     no  \n",
       "45182    yes  \n",
       "45183     no  \n",
       "45184     no  \n",
       "45185    yes  \n",
       "45186     no  \n",
       "45187    yes  \n",
       "45188    yes  \n",
       "45189     no  \n",
       "45190    yes  \n",
       "45191    yes  \n",
       "45192    yes  \n",
       "45193    yes  \n",
       "45194     no  \n",
       "45195    yes  \n",
       "45196    yes  \n",
       "45197    yes  \n",
       "45198     no  \n",
       "45199     no  \n",
       "45200    yes  \n",
       "45201    yes  \n",
       "45202    yes  \n",
       "45203    yes  \n",
       "45204    yes  \n",
       "45205    yes  \n",
       "45206    yes  \n",
       "45207    yes  \n",
       "45208    yes  \n",
       "45209     no  \n",
       "45210     no  \n",
       "\n",
       "[45211 rows x 17 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "\n",
    "bank_data[['balance']].mean()\n",
    "\n",
    "bank_data['balance_outliers'] = bank_data['balance']\n",
    "bank_data['balance_outliers']= zscore(bank_data['balance_outliers'])\n",
    "\n",
    "condition1 = (bank_data['balance_outliers']>3) | (bank_data['balance_outliers']<-3 )\n",
    "dataset3 = bank_data.drop(bank_data[condition1].index, axis = 0, inplace = False)\n",
    "\n",
    "bank_df = bank_data.drop('balance_outliers', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39922\n",
       "1     5289\n",
       "Name: response_binary, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_df.rename(index=str, columns={'Target': 'response1'}, inplace = True)\n",
    "\n",
    "def convert(bank_df, new_column, old_column):\n",
    "    bank_df[new_column] = bank_df[old_column].apply(lambda x: 0 if x == 'no' else 1)\n",
    "    return bank_df[new_column].value_counts()\n",
    "\n",
    "convert(bank_df, \"response_binary\", \"response1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_df1 = bank_df.drop('contact', axis=1)\n",
    "bank_df1['duration'] = bank_df1['duration'].apply(lambda n:n/60).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping customer calls which lasted less than 5 seconds. \n",
    "#This might not have been a proper conversation\n",
    "condition2 = (bank_df1['duration']<5/60)\n",
    "bank_df2 = bank_df1.drop(bank_df1[condition2].index, axis = 0, inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SOjs9xULTt3zdx+a7C5IkSZoijyBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEvSApDkiUmuTvLFJDcl+f1Wf3+SO9qPNl2f5LBWT5Jzk2xOckOSI7p5rUlyW3us6eovSPKlNs25STL3aypJ88+7WEjSwvAQcExVfSvJE4DPJvl4G/dfq+rSce1PAFa0x1HAe4CjkuwHnAmsBAq4NsmGqrq/tVkLfJ7hh51WAR9HkvYwHkGWpAWgBt9qg09oj9rBJKuBC9t0nweWJDkQOB7YWFXbWijeCKxq455WVZ+rqgIuBE7abSskSSPMgCxJC0SSRUmuB+5jCLlXtVFntdMozkmyd6sdBNzVTb6l1XZU3zJBXZL2OAZkSVogqurhqjoMWAYcmeT5wBnATwE/C+wHvKk1n+j84ZpG/VGSrE2yKcmmrVu3TmMtJGn0GZAlaYGpqgeATwOrquqedhrFQ8CfAUe2ZluAg7vJlgF376S+bIL6+GWfV1Urq2rl0qVLZ2mNJGm0GJAlaQFIsjTJkvb8ScBLgS+3c4dpd5w4CbixTbIBOLXdzeJo4MGquge4Ajguyb5J9gWOA65o476Z5Og2r1OBy+ZyHSVpVHgXC0laGA4ELkiyiOHgxiVV9dEkn0yylOEUieuBX2/tLwdOBDYD3wFeC1BV25K8FbimtXtLVW1rz18HvB94EsPdK7yDhaQ9kgFZkhaAqroBOHyC+jGTtC/g9EnGrQfWT1DfBDx/Zj2VpIXPUywkSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkzk4DcpL1Se5LcmNX2y/JxiS3tb/7tnqSnJtkc5IbkhzRTbOmtb8tyZqu/oIkX2rTnJsks72SkiRJ0q7alSPI7wdWjautA66sqhXAlW0Y4ARgRXusBd4DQ6AGzgSOAo4EzhwL1a3N2m668cuSJEmS5sxOA3JVfQbYNq68GrigPb8AOKmrX1iDzwNLkhwIHA9srKptVXU/sBFY1cY9rao+V1UFXNjNS5IkSZpz0z0H+ZlVdQ9A+/uMVj8IuKtrt6XVdlTfMkF9QknWJtmUZNPWrVun2XVJkiRpcrN9kd5E5w/XNOoTqqrzqmplVa1cunTpNLsoSZIkTW66AfnednoE7e99rb4FOLhrtwy4eyf1ZRPUJUmSpHkx3YC8ARi7E8Ua4LKufmq7m8XRwIPtFIwrgOOS7NsuzjsOuKKN+2aSo9vdK07t5iVJapI8McnVSb6Y5KYkv9/qhyS5qt0h6OIke7X63m14cxu/vJvXGa1+a5Lju/qqVtucZN34PkjSnmJXbvP2IeBzwHOSbElyGnA28AtJbgN+oQ0DXA7cDmwG3gv8J4Cq2ga8FbimPd7SagCvA97XpvkK8PHZWTVJelx5CDimqn4GOIzhQuejgbcD57S7Ct0PnNbanwbcX1U/AZzT2pHkUOBk4HkMdw16d5JFSRYB72K4G9GhwCmtrSTtcRbvrEFVnTLJqGMnaFvA6ZPMZz2wfoL6JuD5O+uHJO3J2v71W23wCe1RwDHAr7T6BcCbGW6fubo9B7gUeGf7pm41cFFVPQTckWQzw+03ATZX1e0ASS5qbW/efWslSaPJX9KTpAWiHem9nuG6j40M37o9UFXbW5P+TkA/vHtQG/8gsD9Tv9vQ+D54NyFJj3sGZElaIKrq4ao6jOGC5iOB507UrP3dLXcV8m5CkvYEBmRJWmCq6gHg08DRDD/INHa6XH8noB/ePaiNfzrDjz5N9W5DkrTHMSBL0gKQZGmSJe35k4CXArcAnwJe2ZqNv6vQ2N2GXgl8sp3HvAE4ud3l4hBgBXA1wwXUK9pdMfZiuJBvw+5fM0kaPTu9SE+SNBIOBC5od5v4EeCSqvpokpuBi5K8DbgOOL+1Px/4QLsIbxtD4KWqbkpyCcPFd9uB06vqYYAkr2e4LeciYH1V3TR3qydJo8OALEkLQFXdABw+Qf12HrkLRV//LvCqSeZ1FnDWBPXLGW7XKUl7NE+xkCRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAlqQFIMnBST6V5JYkNyV5Q6u/Ock/J7m+PU7spjkjyeYktyY5vquvarXNSdZ19UOSXJXktiQXJ9lrbtdSkkaDAVmSFobtwG9X1XOBo4HTkxzaxp1TVYe1x+UAbdzJwPOAVcC7kyxKsgh4F3ACcChwSjeft7d5rQDuB06bq5WTpFFiQJakBaCq7qmqL7Tn3wRuAQ7awSSrgYuq6qGqugPYDBzZHpur6vaq+h5wEbA6SYBjgEvb9BcAJ+2etZGk0WZAlqQFJsly4HDgqlZ6fZIbkqxPsm+rHQTc1U22pdUmq+8PPFBV28fVxy97bZJNSTZt3bp1ltZIkkaLAVmSFpAk+wAfBt5YVd8A3gP8OHAYcA/wR2NNJ5i8plF/dKHqvKpaWVUrly5dOo01kKTRN6OAnOQ328UiNyb5UJInTnaRR5K92/DmNn55N58JLySRJD0iyRMYwvEHq+ojAFV1b1U9XFU/AN7LcAoFDEeAD+4mXwbcvYP614ElSRaPq0vSHmfaATnJQcBvACur6vnAIoYLQia7yOM04P6q+gngnNZu0gtJptsvSXo8aucInw/cUlXv6OoHds1+CbixPd8AnNwOThwCrACuBq4BVrSDGXsx7H83VFUBnwJe2aZfA1y2O9dJkkbVTE+xWAw8qR1xeDLD13uTXeSxug3Txh/bdviTXUgiSXrEi4DXAMeMu6XbHyT5UpIbgJ8HfhOgqm4CLgFuBv4WOL0dad4OvB64guFCv0taW4A3Ab+VZDPDOcnnz+H6SdLIWLzzJhOrqn9O8ofAV4F/BT4BXMvkF3n88MKQqtqe5EGGHfBBwOe7WU94YQgMF4cAawF+9Ed/dLpd1yxYvu5jc7asO89+2ZwtSxpVVfVZJj5P+PIdTHMWcNYE9csnmq6qbscDFJI0o1Ms9mU4+nsI8CzgKQz31Rxv7CKPGV0YAl4cIkmSpN1vJqdYvBS4o6q2VtX3gY8AP8fkF3n88MKQNv7pwDYmv2BEkiRJmnMzCchfBY5O8uR2LvGxDOe6TXaRx4Y2TBv/yXZRyGQXkkiSJElzbibnIF+V5FLgCww/gXodcB7wMeCiJG9rtbGLPM4HPtAu/tjGcOU0VXVTkrELSbbTLiSZbr8kSZKkmZh2QAaoqjOBM8eVJ7zIo6q+C7xqkvlMeCGJJEmSNNf8JT1JkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOovnuwPSzixf97E5Xd6dZ79sTpcnSZJGi0eQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkiRJ6hiQJUmSpI4BWZIkSeoYkCVJkqSOAVmSJEnqGJAlSZKkjgFZkhaAJAcn+VSSW5LclOQNrb5fko1Jbmt/9231JDk3yeYkNyQ5opvXmtb+tiRruvoLknypTXNuksz9mkrS/DMgS9LCsB347ap6LnA0cHqSQ4F1wJVVtQK4sg0DnACsaI+1wHtgCNTAmcBRwJHAmWOhurVZ2023ag7WS5JGjgFZkhaAqrqnqr7Qnn8TuAU4CFgNXNCaXQCc1J6vBi6sweeBJUkOBI4HNlbVtqq6H9gIrGrjnlZVn6uqAi7s5iVJexQDsiQtMEmWA4cDVwHPrKp7YAjRwDNas4OAu7rJtrTajupbJqiPX/baJJuSbNq6detsrI4kjRwDsiQtIEn2AT4MvLGqvrGjphPUahr1RxeqzquqlVW1cunSpbvSZUlacAzIkrRAJHkCQzj+YFV9pJXvbadH0P7e1+pbgIO7yZcBd++kvmyCuiTtcQzIkrQAtDtKnA/cUlXv6EZtAMbuRLEGuKyrn9ruZnE08GA7BeMK4Lgk+7aL844Drmjjvpnk6LasU7t5SdIeZfF8d0CStEteBLwG+FKS61vtd4GzgUuSnAZ8FXhVG3c5cCKwGfgO8FqAqtqW5K3ANa3dW6pqW3v+OuD9wJOAj7eHJO1xDMiStABU1WeZ+DxhgGMnaF/A6ZPMaz2wfoL6JuD5M+imJD0ueIqFJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUmVFATrIkyaVJvpzkliQvTLJfko1Jbmt/921tk+TcJJuT3JDkiG4+a1r725KsmXyJkiRJ0u410yPI/wP426r6KeBngFuAdcCVVbUCuLINA5wArGiPtcB7AJLsB5wJHAUcCZw5FqolSZKkuTbtgJzkacCLGX7Ziar6XlU9AKwGLmjNLgBOas9XAxfW4PPAkvazqMcDG6tqW1XdD2wEVk23X5IkSdJMzOQI8rOBrcCfJbkuyfuSPAV4ZvvJUtrfZ7T2BwF3ddNvabXJ6pIkSdKcm0lAXgwcAbynqg4Hvs0jp1NMZKJfgKod1B87g2Rtkk1JNm3dunWq/ZUkSZJ2aiYBeQuwpaquasOXMgTme9upE7S/93XtD+6mXwbcvYP6Y1TVeVW1sqpWLl26dAZdlyRJkiY27YBcVV8D7krynFY6FrgZ2ACM3YliDXBZe74BOLXdzeJo4MF2CsYVwHFJ9m0X5x3XapIkSdKcWzzD6f8z8MEkewG3A69lCN2XJDkN+Crwqtb2cuBEYDPwndaWqtqW5K3ANa3dW6pq2wz7JUmSJE3LjAJyVV0PrJxg1LETtC3g9Enmsx5YP5O+SJIkSbPBX9KTJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJakBSDJ+iT3Jbmxq705yT8nub49TuzGnZFkc5Jbkxzf1Ve12uYk67r6IUmuSnJbkouT7DV3aydJo8WALEkLw/uBVRPUz6mqw9rjcoAkhwInA89r07w7yaIki4B3AScAhwKntLYAb2/zWgHcD5y2W9dGkkaYAVmSFoCq+gywbRebrwYuqqqHquoOYDNwZHtsrqrbq+p7wEXA6iQBjgEubdNfAJw0qysgSQuIAVmSFrbXJ7mhnYKxb6sdBNzVtdnSapPV9wceqKrt4+qPkWRtkk1JNm3dunU210OSRoYBWZIWrvcAPw4cBtwD/FGrZ4K2NY36Y4tV51XVyqpauXTp0qn3WJIWgMXz3QFJ0vRU1b1jz5O8F/hoG9wCHNw1XQbc3Z5PVP86sCTJ4nYUuW8vSXscjyBL0gKV5MBu8JeAsTtcbABOTrJ3kkOAFcDVwDXAinbHir0YLuTbUFUFfAp4ZZt+DXDZXKyDJI0ijyBL0gKQ5EPAS4ADkmwBzgRekuQwhtMh7gR+DaCqbkpyCXAzsB04vaoebvN5PXAFsAhYX1U3tUW8CbgoyduA64Dz52jVJGnkGJAlaQGoqlMmKE8aYqvqLOCsCeqXA5eK3GgwAAARG0lEQVRPUL+d4S4XkrTH8xQLSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6Mw7ISRYluS7JR9vwIUmuSnJbkouT7NXqe7fhzW388m4eZ7T6rUmOn2mfJEmSpOmajSPIbwBu6YbfDpxTVSuA+4HTWv004P6q+gngnNaOJIcCJwPPA1YB706yaBb6JUmSJE3ZjAJykmXAy4D3teEAxwCXtiYXACe156vbMG38sa39auCiqnqoqu4ANgNHzqRfkiRJ0nTN9AjyHwO/A/ygDe8PPFBV29vwFuCg9vwg4C6ANv7B1v6H9QmmeZQka5NsSrJp69atM+y6JEmS9FjTDshJXg7cV1XX9uUJmtZOxu1omkcXq86rqpVVtXLp0qVT6q8kSZK0KxbPYNoXAa9IciLwROBpDEeUlyRZ3I4SLwPubu23AAcDW5IsBp4ObOvqY/ppJEmSpDk17SPIVXVGVS2rquUMF9l9sqpeDXwKeGVrtga4rD3f0IZp4z9ZVdXqJ7e7XBwCrACunm6/JEmSpJmYyRHkybwJuCjJ24DrgPNb/XzgA0k2Mxw5Phmgqm5KcglwM7AdOL2qHt4N/ZIkSZJ2alYCclV9Gvh0e347E9yFoqq+C7xqkunPAs6ajb5IkiRJM+Ev6UmSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEuSJEkdA7IkSZLUMSBLkiRJHQOyJEmS1DEgS5IkSR0DsiRJktQxIEvSApBkfZL7ktzY1fZLsjHJbe3vvq2eJOcm2ZzkhiRHdNOsae1vS7Kmq78gyZfaNOcmydyuoSSNDgOyJC0M7wdWjautA66sqhXAlW0Y4ARgRXusBd4DQ6AGzgSOAo4EzhwL1a3N2m668cuSpD3G4vnugDRqlq/72Jwt686zXzZny9LCVlWfSbJ8XHk18JL2/ALg08CbWv3Cqirg80mWJDmwtd1YVdsAkmwEViX5NPC0qvpcq18InAR8fPetkSSNLo8gS9LC9cyqugeg/X1Gqx8E3NW129JqO6pvmaD+GEnWJtmUZNPWrVtnZSUkadQYkCXp8Wei84drGvXHFqvOq6qVVbVy6dKlM+iiJI0uA7IkLVz3tlMnaH/va/UtwMFdu2XA3TupL5ugLkl7JAOyJC1cG4CxO1GsAS7r6qe2u1kcDTzYTsG4Ajguyb7t4rzjgCvauG8mObrdveLUbl6StMfxIj1JWgCSfIjhIrsDkmxhuBvF2cAlSU4Dvgq8qjW/HDgR2Ax8B3gtQFVtS/JW4JrW7i1jF+wBr2O4U8aTGC7O8wI9SXssA7IkLQBVdcoko46doG0Bp08yn/XA+gnqm4Dnz6SPkvR44SkWkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElSx4AsSZIkdQzIkiRJUseALEmSJHWmHZCTHJzkU0luSXJTkje0+n5JNia5rf3dt9WT5Nwkm5PckOSIbl5rWvvbkqyZ+WpJkiRJ0zOTI8jbgd+uqucCRwOnJzkUWAdcWVUrgCvbMMAJwIr2WAu8B4ZADZwJHAUcCZw5FqolSZKkuTbtgFxV91TVF9rzbwK3AAcBq4ELWrMLgJPa89XAhTX4PLAkyYHA8cDGqtpWVfcDG4FV0+2XJEmSNBOzcg5ykuXA4cBVwDOr6h4YQjTwjNbsIOCubrItrTZZfaLlrE2yKcmmrVu3zkbXJUmSpEeZcUBOsg/wYeCNVfWNHTWdoFY7qD+2WHVeVa2sqpVLly6demclSZKknZhRQE7yBIZw/MGq+kgr39tOnaD9va/VtwAHd5MvA+7eQV2SJEmaczO5i0WA84Fbquod3agNwNidKNYAl3X1U9vdLI4GHmynYFwBHJdk33Zx3nGtJkmSJM25xTOY9kXAa4AvJbm+1X4XOBu4JMlpwFeBV7VxlwMnApuB7wCvBaiqbUneClzT2r2lqrbNoF+SJEnStE07IFfVZ5n4/GGAYydoX8Dpk8xrPbB+un2RJEmSZou/pCdJkiR1DMiStMAluTPJl5Jcn2RTq/mrppI0TTM5B1mSNDp+vqq+3g2P/arp2UnWteE38ehfNT2K4VdNj+p+1XQlw602r02yof2A04J19nVf33mjWbTu8APmdHmSdg+PIEvS45O/aipJ02RAlqSFr4BPJLk2ydpW2y2/auovmkraE3iKhSQtfC+qqruTPAPYmOTLO2g7o181rarzgPMAVq5cOeGvnkrSQucRZEla4Krq7vb3PuCvgCPxV00ladoMyJK0gCV5SpKnjj1n+DXSG/FXTSVp2jzFQpIWtmcCf5UEhn36X1TV3ya5Bn/VVJKmxYAsSQtYVd0O/MwE9X/BXzWVpGnxFAtJkiSpY0CWJEmSOgZkSZIkqbNHnYO8fN3H5rsLkiRJGnEeQZYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOgZkSZIkqWNAliRJkjoGZEmSJKljQJYkSZI6i+e7A9KebPm6j83Zsu48+2VztixJkhYyjyBLkiRJHY8gS3uIuTxaDR6xliQtXB5BliRJkjoGZEmSJKljQJYkSZI6BmRJkiSpY0CWJEmSOiNzF4skq4D/ASwC3ldVZ89zlyTNgPd4XpjcF8/M2dd9fc6Wte7wA+ZsWdKeZiSOICdZBLwLOAE4FDglyaHz2ytJ2rO4L5akwagcQT4S2FxVtwMkuQhYDdw8r72StCB4j+dZ4754AZnLo9XgEWvtWUYlIB8E3NUNbwGOGt8oyVpgbRv8VpJbZ7kfBwBzu8eZ3Cj1BUarP6PUF7A/OzJKfYFZ6k/ePu1Jf2ymy97NdrovnoP98HSM2vtsukZ6Pc7Y9aYjvR676PGwDuB6TGSX9sOjEpAzQa0eU6g6Dzhvt3Ui2VRVK3fX/KdilPoCo9WfUeoL2J8dGaW+wOj1ZwTtdF+8u/fD0/F42a6ux+h4PKwDuB4zMRLnIDMcpTi4G14G3D1PfZGkPZX7YklidALyNcCKJIck2Qs4Gdgwz32SpD2N+2JJYkROsaiq7UleD1zBcGuh9VV10zx0ZZS+NhylvsBo9WeU+gL2Z0dGqS8wev0ZKSO0L56qx8t2dT1Gx+NhHcD1mLZUPeZUX0mSJGmPNSqnWEiSJEkjwYAsSZIkdfbIgJzk4CSfSnJLkpuSvKHV90uyMclt7e++c9SfJya5OskXW39+v9UPSXJV68/F7aKZOZFkUZLrknx0BPpyZ5IvJbk+yaZWm5dt1Za9JMmlSb7c3kMvnI/+JHlOe03GHt9I8sZ5fm1+s72Hb0zyofbenpf3TpI3tH7clOSNrTZvr41mX5JVSW5NsjnJuvnuD0z98yWDc9s63JDkiG5ea1r725Ks6eovaPvEzW3aiW7PNxvrskufA0n2bsOb2/jl3TzOaPVbkxzf1eds201lnz2q22Mq+9ZR2h5J1ie5L8mNXW23v/aTLWNKqmqPewAHAke0508F/pHhZ1X/AFjX6uuAt89RfwLs054/AbgKOBq4BDi51f8EeN0cvka/BfwF8NE2PJ99uRM4YFxtXrZVW94FwH9sz/cClsxnf9oyFwFfY7gB+ny9jw8C7gCe1L1nfnU+3jvA84EbgSczXIz8P4EV872dfMzqNl4EfAV4dvt3+EXg0BHo15Q+X4ATgY+3z4GjgatafT/g9vZ33/Z83zbuauCFbZqPAyfspnXZpc8B4D8Bf9Kenwxc3J4f2rbL3sAhbXstmuttN5V99ihuj6nuW0dpewAvBo4Abuxqu/21n2wZU+r77npDLqQHcBnwC8CtwIGtdiBw6zz05cnAFxh+verrwOJWfyFwxRz1YRlwJXAM8NH2xpuXvrTl3cljA/K8bCvgaW1HlVHoT7f844C/n+fXZuxX2PZjCKUfBY6fj/cO8Crgfd3w/wX8znxvJx+zuo0f9V5i+KG3M+a7XxP0c4efL8CfAqd07W9t408B/rSr/2mrHQh8uas/qt0s9nuXPwcY7nrywvZ8cWuX8dtkrN1cbrup7rNHcXtMdd86atsDWM6jA/Juf+0nW8ZUHnvkKRa99tXD4QxHbZ9ZVfcAtL/PmMN+LEpyPXAfsJHhf3MPVNX21mQLwz+SufDHDGHiB214/3nsCwy/5PWJJNdm+JlbmL9t9WxgK/Bn7avH9yV5yjz2Z8zJwIfa83npS1X9M/CHwFeBe4AHgWuZn/fOjcCLk+yf5MkMRyYOZv63k2bPRD+LPZf7pZ3axc+XydZjR/UtE9Rn21Q+B37Y1zb+wdZ+quu2O0x1nz1y22Ma+9ZR3h4wN6/9jPf1e3RATrIP8GHgjVX1jfnsS1U9XFWHMfyv/UjguRM12939SPJy4L6qurYvz0dfOi+qqiOAE4DTk7x4Dpc93mKGr4veU1WHA99m+Ppm3rTzzl4B/OU892NfYDXDV3fPAp7CsM3G2+3vnaq6BXg7w382/5bhK8PtO5xIC81875d2aAqfL5Otx1Trs2YanwMjtw6dqe6zR25dprFvHbl12EUj1e89NiAneQLDzuuDVfWRVr43yYFt/IEMR3PnVFU9AHya4fybJUnGfsxlrn7y9UXAK5LcCVzE8PXaH89TXwCoqrvb3/uAv2L4D8R8bastwJaquqoNX8qw853P984JwBeq6t42PF99eSlwR1VtrarvAx8Bfo55eu9U1flVdURVvRjYBtzGCPwb16wZ2Z/FnuLny2TrsaP6sgnqs2mqnwM/7Gsb/3SGf3NTXbfdYar77FHcHlPdt47y9oC5ee1nvK/fIwNyu8rxfOCWqnpHN2oDsKY9X8Nw7thc9GdpkiXt+ZMY/jHcAnwKeOVc9qeqzqiqZVW1nOFr+09W1avnoy8ASZ6S5KljzxnOtb2RedpWVfU14K4kz2mlY4Gb56s/zSk8cnoF89iXrwJHJ3ly+zc29trM13vnGe3vjwK/zPAazed20uwayZ/Fnsbnywbg1HYF/9HAg+0r4SuA45Ls244gHsdwnug9wDeTHN2WdSqz/D6exudAv26vbO2r1U9ud1U4hOFC2auZw203jX32yG0Ppr5vHdntMUH/dtdrP/N9/WydhL2QHsC/ZTgMfwNwfXucyHCOzpUMR5quBPabo/78NHBd68+NwH9r9WczvHk3M3x9vvccv04v4ZGrl+elL225X2yPm4Dfa/V52VZt2YcBm9r2+muGq2rn673zZOBfgKd3tfl8bX4f+HJ7H3+A4Wrp+Xrv/B3Dh8gXgWPn+7XxsVu28YkMd4n4yti+Yb4fU/18Yfia+F1tHb4ErOzm9R/av5vNwGu7+sr2b+wrwDsZdwHaLK/PTj8HgCe24c1t/LO76X+v9fNWurs7zOW2m8o+e1S3x1T2raO0PRgOTNwDfJ/hiO9pc/HaT7aMqTz8qWlJkiSps0eeYiFJkiRNxoAsSZIkdQzIkiRJUseALEmSJHUMyJIkSVLHgCxJkiR1DMiSJElS5/8HVH1kelxt52AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_age_balance = plt.figure(figsize = (10,6))\n",
    "\n",
    "ra1 = dist_age_balance.add_subplot(1,2,1) \n",
    "ra2 = dist_age_balance.add_subplot(1,2,2)\n",
    "\n",
    "ra1.hist(bank_df2['age'])\n",
    "ra1.set_title('The Distribution of Age')\n",
    "\n",
    "ra2.hist(bank_df2['balance'], color = 'skyblue')\n",
    "ra2.set_title('The Distribution of Balance')\n",
    "\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of age: \n",
    "In the telemarketing campaigns, clients called by the bank have an extensive age range, from 18 to 95 years old. However, a majority of customers called is in the age of 30s and 40s(25th-Q3 percentile. The distribution of customer age is fairly normal with a small standard deviation.\n",
    "\n",
    "The distribution of balance: After dropping outliers in balance, the range of balance is still massive, from a negative value to a maximum of 16000 euros. The distribution of balance has a huge standard deviation relative to the mean, suggesting large variabilities in customers' balance levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_age_balance = bank_df2.plot.scatter('age','balance',figsize = (7,5))\n",
    "\n",
    "plt.title('The Relationship between Age and Balance ')\n",
    "plt.show()\n",
    "#Customers with age >=60, have a lower income base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 563.875x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dur_cam = sns.lmplot(x='duration', y='campaign',data = bank_df2,\n",
    "                     hue = 'response1',\n",
    "                     fit_reg = False,\n",
    "                     scatter_kws={'alpha':0.6}, height =7)\n",
    "\n",
    "plt.axis([0,65,0,65])\n",
    "plt.ylabel('Number of Calls')\n",
    "plt.xlabel('Duration of Calls (Min.)')\n",
    "plt.title('The Relationship between the # and Duration of Calls (with Response Result)')\n",
    "\n",
    "# Annotation\n",
    "plt.axhline(y=5, linewidth=2, color=\"k\", linestyle='--')\n",
    "plt.annotate('Higher subscription rate when calls <5',xytext = (35,13),\n",
    "             arrowprops=dict(color = 'k', width=1),xy=(30,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, “yes” clients and “no” clients are forming two relatively separate clusters. Compared to “no” clients”, “yes” clients were contacted by fewer times and had longer call duration. More importantly, after five campaign calls, clients are more likely to reject the term deposit unless the duration is high. Most “yes” clients were approached by less than 10 times.\n",
    "\n",
    "This suggests that the bank should resist calling a client for more than five times, which can be disturbing and increase dissatisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "matrix = scatter_matrix(bank_df2[['age','balance','duration','campaign']],figsize=(10,8))\n",
    "\n",
    "plt.suptitle('The Scatter Matrix of Age, Balance, Duration and Campaign')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank_df_corr = bank_df2.corr()\n",
    "bank_df_corr = sns.heatmap(bank_df_corr,annot=True,cmap='RdYlGn',linewidths=0.2,annot_kws={'size':10})\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(6,5)\n",
    "plt.xticks(fontsize=10,rotation=-30)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation matrix  plotted with all qualitative variables. Clearly, “campaign outcome” has a strong correlation with “duration”, a moderate correlation with “previous contacts”, and mild correlations between “balance”, “month of contact” and “number of campaign”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [bank_df2]\n",
    "for column in lst:\n",
    "    column.loc[column[\"age\"] < 30,  'age_group'] = 20\n",
    "    column.loc[(column[\"age\"] >= 30) & (column[\"age\"] <= 39), 'age_group'] = 30\n",
    "    column.loc[(column[\"age\"] >= 40) & (column[\"age\"] <= 49), 'age_group'] = 40\n",
    "    column.loc[(column[\"age\"] >= 50) & (column[\"age\"] <= 59), 'age_group'] = 50\n",
    "    column.loc[column[\"age\"] >= 60, 'age_group'] = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [bank_df2]\n",
    "for column in lst:\n",
    "    column.loc[column[\"balance\"] <= 0,  'balance_group'] = 'no balance'\n",
    "    column.loc[(column[\"balance\"] > 0) & (column[\"balance\"] <= 1000), 'balance_group'] = 'low balance'\n",
    "    column.loc[(column[\"balance\"] > 1000) & (column[\"balance\"] <= 5000), 'balance_group'] = 'average balance'\n",
    "    column.loc[(column[\"balance\"] > 5000), 'balance_group'] = 'high balance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Subscription Rate of Different Balance Levels in Each Age Group')"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_balance1 = pd.DataFrame(bank_df2.groupby(['age_group','balance_group'])['response_binary'].sum())\n",
    "age_balance2 = pd.DataFrame(bank_df2.groupby(['age_group','balance_group'])['response1'].count())\n",
    "\n",
    "age_balance1['response1'] = age_balance2['response1']\n",
    "age_balance1['response_rate'] = age_balance1['response_binary']/ (age_balance1['response1'])\n",
    "age_balance1 = age_balance1.drop(['response_binary','response1'],axis =1)\n",
    "\n",
    "age_balance1 = age_balance1.unstack()\n",
    "\n",
    "age_bal = age_balance1.plot(kind='bar',figsize = (10,6))\n",
    "\n",
    "# Set x ticks\n",
    "plt.xticks(np.arange(5),('<30', '30-39', '40-49', '50-59', '60+'),rotation = 'horizontal')\n",
    "\n",
    "# Set legend\n",
    "plt.legend(['Average Balance','High Balance','Low Balance','No Balance'],loc = 'best',ncol = 1)\n",
    "\n",
    "plt.ylabel('Subscription Rate')\n",
    "plt.xlabel('Age Group')\n",
    "plt.title('The Subscription Rate of Different Balance Levels in Each Age Group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interest in subscription for deposit is exceptionally high for people aged above 60 and younger people aged below 30 also have a distinguishable higher subscription rate than those of other age groups.\n",
    "\n",
    "The effect of balance levels on subscription decision is applicable to each individual age group: every age group shares a common trend that the percentage of subscription increases with balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bank_data = bank_data.drop(['Unnamed: 0'], axis=1)\n",
    "\n",
    "bank_df2.drop(['marital'],axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1 = bank_df2.iloc[:, 0:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>job_admin.</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_management</th>\n",
       "      <th>...</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_unemployed</th>\n",
       "      <th>education_other</th>\n",
       "      <th>education_primary</th>\n",
       "      <th>education_secondary</th>\n",
       "      <th>education_tertiary</th>\n",
       "      <th>response_binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\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>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>447</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>593</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>229</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>723</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>779</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>-372</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>-246</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>265</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45181</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>6879</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45182</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45183</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45184</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1495</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45185</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>4256</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45186</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45187</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1168</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45188</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>703</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45189</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45190</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45191</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>3810</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45192</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>765</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45193</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45194</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45195</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45196</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>358</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45197</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1511</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45198</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1428</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45199</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1475</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45200</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>557</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45201</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45202</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>557</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45203</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45204</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>2850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45205</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1729</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>5715</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>668</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45149 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  default  balance  housing  loan  job_admin.  job_blue-collar  \\\n",
       "0       58        0     2143        1     0           0                0   \n",
       "1       44        0       29        1     0           0                0   \n",
       "2       33        0        2        1     1           0                0   \n",
       "3       47        0     1506        1     0           0                1   \n",
       "4       33        0        1        0     0           0                0   \n",
       "5       35        0      231        1     0           0                0   \n",
       "6       28        0      447        1     1           0                0   \n",
       "7       42        1        2        1     0           0                0   \n",
       "8       58        0      121        1     0           0                0   \n",
       "9       43        0      593        1     0           0                0   \n",
       "10      41        0      270        1     0           1                0   \n",
       "11      29        0      390        1     0           1                0   \n",
       "12      53        0        6        1     0           0                0   \n",
       "13      58        0       71        1     0           0                0   \n",
       "14      57        0      162        1     0           0                0   \n",
       "15      51        0      229        1     0           0                0   \n",
       "16      45        0       13        1     0           1                0   \n",
       "17      57        0       52        1     0           0                1   \n",
       "18      60        0       60        1     0           0                0   \n",
       "19      33        0        0        1     0           0                0   \n",
       "20      28        0      723        1     1           0                1   \n",
       "21      56        0      779        1     0           0                0   \n",
       "22      32        0       23        1     1           0                1   \n",
       "23      25        0       50        1     0           0                0   \n",
       "24      40        0        0        1     1           0                0   \n",
       "25      44        0     -372        1     0           1                0   \n",
       "26      39        0      255        1     0           0                0   \n",
       "27      52        0      113        1     1           0                0   \n",
       "28      46        0     -246        1     0           0                0   \n",
       "29      36        0      265        1     1           0                0   \n",
       "...    ...      ...      ...      ...   ...         ...              ...   \n",
       "45181   46        0     6879        0     0           0                1   \n",
       "45182   34        0      133        0     0           0                0   \n",
       "45183   70        0      324        0     0           0                0   \n",
       "45184   63        0     1495        0     0           0                0   \n",
       "45185   60        0     4256        1     0           0                0   \n",
       "45186   59        0     1500        0     0           0                0   \n",
       "45187   32        0     1168        1     0           0                0   \n",
       "45188   29        0      703        1     0           0                0   \n",
       "45189   25        0      199        0     0           0                0   \n",
       "45190   32        0      136        0     0           0                1   \n",
       "45191   75        0     3810        1     0           0                0   \n",
       "45192   29        0      765        0     0           0                0   \n",
       "45193   28        0      159        0     0           0                0   \n",
       "45194   59        0      138        1     1           0                0   \n",
       "45195   68        0     1146        0     0           0                0   \n",
       "45196   25        0      358        0     0           0                0   \n",
       "45197   36        0     1511        1     0           0                0   \n",
       "45198   37        0     1428        0     0           0                0   \n",
       "45199   34        0     1475        1     0           0                1   \n",
       "45200   38        0      557        1     0           0                0   \n",
       "45201   53        0      583        0     0           0                0   \n",
       "45202   34        0      557        0     0           1                0   \n",
       "45203   23        0      113        0     0           0                0   \n",
       "45204   73        0     2850        0     0           0                0   \n",
       "45205   25        0      505        0     1           0                0   \n",
       "45206   51        0      825        0     0           0                0   \n",
       "45207   71        0     1729        0     0           0                0   \n",
       "45208   72        0     5715        0     0           0                0   \n",
       "45209   57        0      668        0     0           0                1   \n",
       "45210   37        0     2971        0     0           0                0   \n",
       "\n",
       "       job_entrepreneur  job_housemaid  job_management       ...         \\\n",
       "0                     0              0               1       ...          \n",
       "1                     0              0               0       ...          \n",
       "2                     1              0               0       ...          \n",
       "3                     0              0               0       ...          \n",
       "4                     0              0               0       ...          \n",
       "5                     0              0               1       ...          \n",
       "6                     0              0               1       ...          \n",
       "7                     1              0               0       ...          \n",
       "8                     0              0               0       ...          \n",
       "9                     0              0               0       ...          \n",
       "10                    0              0               0       ...          \n",
       "11                    0              0               0       ...          \n",
       "12                    0              0               0       ...          \n",
       "13                    0              0               0       ...          \n",
       "14                    0              0               0       ...          \n",
       "15                    0              0               0       ...          \n",
       "16                    0              0               0       ...          \n",
       "17                    0              0               0       ...          \n",
       "18                    0              0               0       ...          \n",
       "19                    0              0               0       ...          \n",
       "20                    0              0               0       ...          \n",
       "21                    0              0               1       ...          \n",
       "22                    0              0               0       ...          \n",
       "23                    0              0               0       ...          \n",
       "24                    0              0               0       ...          \n",
       "25                    0              0               0       ...          \n",
       "26                    0              0               1       ...          \n",
       "27                    1              0               0       ...          \n",
       "28                    0              0               1       ...          \n",
       "29                    0              0               0       ...          \n",
       "...                 ...            ...             ...       ...          \n",
       "45181                 0              0               0       ...          \n",
       "45182                 0              0               0       ...          \n",
       "45183                 0              0               0       ...          \n",
       "45184                 0              0               0       ...          \n",
       "45185                 0              0               0       ...          \n",
       "45186                 0              0               0       ...          \n",
       "45187                 0              0               0       ...          \n",
       "45188                 0              0               1       ...          \n",
       "45189                 0              0               0       ...          \n",
       "45190                 0              0               0       ...          \n",
       "45191                 0              0               0       ...          \n",
       "45192                 0              0               1       ...          \n",
       "45193                 0              0               0       ...          \n",
       "45194                 0              0               1       ...          \n",
       "45195                 0              0               0       ...          \n",
       "45196                 0              0               0       ...          \n",
       "45197                 0              0               1       ...          \n",
       "45198                 0              0               1       ...          \n",
       "45199                 0              0               0       ...          \n",
       "45200                 0              0               0       ...          \n",
       "45201                 0              0               1       ...          \n",
       "45202                 0              0               0       ...          \n",
       "45203                 0              0               0       ...          \n",
       "45204                 0              0               0       ...          \n",
       "45205                 0              0               0       ...          \n",
       "45206                 0              0               0       ...          \n",
       "45207                 0              0               0       ...          \n",
       "45208                 0              0               0       ...          \n",
       "45209                 0              0               0       ...          \n",
       "45210                 1              0               0       ...          \n",
       "\n",
       "       job_self-employed  job_services  job_student  job_technician  \\\n",
       "0                      0             0            0               0   \n",
       "1                      0             0            0               1   \n",
       "2                      0             0            0               0   \n",
       "3                      0             0            0               0   \n",
       "4                      0             0            0               0   \n",
       "5                      0             0            0               0   \n",
       "6                      0             0            0               0   \n",
       "7                      0             0            0               0   \n",
       "8                      0             0            0               0   \n",
       "9                      0             0            0               1   \n",
       "10                     0             0            0               0   \n",
       "11                     0             0            0               0   \n",
       "12                     0             0            0               1   \n",
       "13                     0             0            0               1   \n",
       "14                     0             1            0               0   \n",
       "15                     0             0            0               0   \n",
       "16                     0             0            0               0   \n",
       "17                     0             0            0               0   \n",
       "18                     0             0            0               0   \n",
       "19                     0             1            0               0   \n",
       "20                     0             0            0               0   \n",
       "21                     0             0            0               0   \n",
       "22                     0             0            0               0   \n",
       "23                     0             1            0               0   \n",
       "24                     0             0            0               0   \n",
       "25                     0             0            0               0   \n",
       "26                     0             0            0               0   \n",
       "27                     0             0            0               0   \n",
       "28                     0             0            0               0   \n",
       "29                     0             0            0               1   \n",
       "...                  ...           ...          ...             ...   \n",
       "45181                  0             0            0               0   \n",
       "45182                  0             0            0               1   \n",
       "45183                  0             0            0               0   \n",
       "45184                  0             0            0               0   \n",
       "45185                  0             1            0               0   \n",
       "45186                  0             0            0               0   \n",
       "45187                  0             1            0               0   \n",
       "45188                  0             0            0               0   \n",
       "45189                  0             1            0               0   \n",
       "45190                  0             0            0               0   \n",
       "45191                  0             0            0               0   \n",
       "45192                  0             0            0               0   \n",
       "45193                  1             0            0               0   \n",
       "45194                  0             0            0               0   \n",
       "45195                  0             0            0               0   \n",
       "45196                  0             0            1               0   \n",
       "45197                  0             0            0               0   \n",
       "45198                  0             0            0               0   \n",
       "45199                  0             0            0               0   \n",
       "45200                  0             0            0               1   \n",
       "45201                  0             0            0               0   \n",
       "45202                  0             0            0               0   \n",
       "45203                  0             0            1               0   \n",
       "45204                  0             0            0               0   \n",
       "45205                  0             0            0               1   \n",
       "45206                  0             0            0               1   \n",
       "45207                  0             0            0               0   \n",
       "45208                  0             0            0               0   \n",
       "45209                  0             0            0               0   \n",
       "45210                  0             0            0               0   \n",
       "\n",
       "       job_unemployed  education_other  education_primary  \\\n",
       "0                   0                0                  0   \n",
       "1                   0                0                  0   \n",
       "2                   0                0                  0   \n",
       "3                   0                1                  0   \n",
       "4                   0                1                  0   \n",
       "5                   0                0                  0   \n",
       "6                   0                0                  0   \n",
       "7                   0                0                  0   \n",
       "8                   0                0                  1   \n",
       "9                   0                0                  0   \n",
       "10                  0                0                  0   \n",
       "11                  0                0                  0   \n",
       "12                  0                0                  0   \n",
       "13                  0                1                  0   \n",
       "14                  0                0                  0   \n",
       "15                  0                0                  1   \n",
       "16                  0                1                  0   \n",
       "17                  0                0                  1   \n",
       "18                  0                0                  1   \n",
       "19                  0                0                  0   \n",
       "20                  0                0                  0   \n",
       "21                  0                0                  0   \n",
       "22                  0                0                  1   \n",
       "23                  0                0                  0   \n",
       "24                  0                0                  1   \n",
       "25                  0                0                  0   \n",
       "26                  0                0                  0   \n",
       "27                  0                0                  0   \n",
       "28                  0                0                  0   \n",
       "29                  0                0                  0   \n",
       "...               ...              ...                ...   \n",
       "45181               0                0                  0   \n",
       "45182               0                0                  0   \n",
       "45183               0                0                  1   \n",
       "45184               0                0                  0   \n",
       "45185               0                0                  0   \n",
       "45186               0                1                  0   \n",
       "45187               0                0                  0   \n",
       "45188               0                0                  0   \n",
       "45189               0                0                  0   \n",
       "45190               0                0                  0   \n",
       "45191               0                0                  0   \n",
       "45192               0                0                  0   \n",
       "45193               0                0                  0   \n",
       "45194               0                0                  0   \n",
       "45195               0                0                  0   \n",
       "45196               0                0                  0   \n",
       "45197               0                0                  0   \n",
       "45198               0                0                  0   \n",
       "45199               0                0                  0   \n",
       "45200               0                0                  0   \n",
       "45201               0                0                  0   \n",
       "45202               0                0                  0   \n",
       "45203               0                0                  0   \n",
       "45204               0                0                  0   \n",
       "45205               0                0                  0   \n",
       "45206               0                0                  0   \n",
       "45207               0                0                  1   \n",
       "45208               0                0                  0   \n",
       "45209               0                0                  0   \n",
       "45210               0                0                  0   \n",
       "\n",
       "       education_secondary  education_tertiary  response_binary  \n",
       "0                        0                   1                0  \n",
       "1                        1                   0                0  \n",
       "2                        1                   0                0  \n",
       "3                        0                   0                0  \n",
       "4                        0                   0                0  \n",
       "5                        0                   1                0  \n",
       "6                        0                   1                0  \n",
       "7                        0                   1                0  \n",
       "8                        0                   0                0  \n",
       "9                        1                   0                0  \n",
       "10                       1                   0                0  \n",
       "11                       1                   0                0  \n",
       "12                       1                   0                0  \n",
       "13                       0                   0                0  \n",
       "14                       1                   0                0  \n",
       "15                       0                   0                0  \n",
       "16                       0                   0                0  \n",
       "17                       0                   0                0  \n",
       "18                       0                   0                0  \n",
       "19                       1                   0                0  \n",
       "20                       1                   0                0  \n",
       "21                       0                   1                0  \n",
       "22                       0                   0                0  \n",
       "23                       1                   0                0  \n",
       "24                       0                   0                0  \n",
       "25                       1                   0                0  \n",
       "26                       0                   1                0  \n",
       "27                       1                   0                0  \n",
       "28                       1                   0                0  \n",
       "29                       1                   0                0  \n",
       "...                    ...                 ...              ...  \n",
       "45181                    1                   0                0  \n",
       "45182                    1                   0                1  \n",
       "45183                    0                   0                0  \n",
       "45184                    1                   0                0  \n",
       "45185                    0                   1                1  \n",
       "45186                    0                   0                0  \n",
       "45187                    1                   0                1  \n",
       "45188                    1                   0                1  \n",
       "45189                    1                   0                0  \n",
       "45190                    1                   0                1  \n",
       "45191                    0                   1                1  \n",
       "45192                    0                   1                1  \n",
       "45193                    0                   1                1  \n",
       "45194                    0                   1                0  \n",
       "45195                    1                   0                1  \n",
       "45196                    1                   0                1  \n",
       "45197                    1                   0                1  \n",
       "45198                    0                   1                0  \n",
       "45199                    1                   0                0  \n",
       "45200                    1                   0                1  \n",
       "45201                    0                   1                1  \n",
       "45202                    1                   0                1  \n",
       "45203                    0                   1                1  \n",
       "45204                    1                   0                1  \n",
       "45205                    1                   0                1  \n",
       "45206                    0                   1                1  \n",
       "45207                    0                   0                1  \n",
       "45208                    1                   0                1  \n",
       "45209                    1                   0                0  \n",
       "45210                    1                   0                0  \n",
       "\n",
       "[45149 rows x 22 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Changing categoical to dummy varaible\n",
    "\n",
    "dataset2 = pd.get_dummies(dataset1, columns = ['job'])\n",
    "dataset2 = pd.get_dummies(dataset2, columns = ['education'])\n",
    "dataset2['housing'] = dataset2['housing'].map({'yes': 1, 'no': 0})\n",
    "dataset2['default'] = dataset2['default'].map({'yes': 1, 'no': 0})\n",
    "dataset2['loan'] = dataset2['loan'].map({'yes': 1, 'no': 0})\n",
    "bank_df2_response = pd.DataFrame(bank_df2['response_binary'])\n",
    "dataset2 = pd.merge(dataset2, bank_df2_response, left_index = True, right_index = True)dataset2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = dataset2.values\n",
    "X = array[:,0:-1]\n",
    "\n",
    "# Target variable: 'response_binary'\n",
    "Y = array[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "# 20% of the data will be used for testing\n",
    "test_size= 0.20\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test= train_test_split(X, Y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.884078 (0.003638)\n",
      "KNN: 0.874083 (0.004167)\n",
      "CART: 0.834076 (0.006617)\n",
      "NB: 0.824386 (0.008316)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "\n",
    "results_c = []\n",
    "names_c = []\n",
    "for name, model in models:\n",
    "    # define how to split off validation data ('kfold' how many folds)\n",
    "    kfold = KFold(n_splits=10, random_state=seed)    \n",
    "    # train the model\n",
    "    \n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv = kfold, scoring = 'accuracy')    \n",
    "   \n",
    "    results_c.append(cv_results)\n",
    "    names_c.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset4 = bank_df2.drop(['response_binary'],axis = 1)\n",
    "dataset4['duration'] = bank_data['duration']\n",
    "\n",
    "array = dataset4.values\n",
    "X = array[:,0:15]\n",
    "Y = array[:,15]\n",
    "\n",
    "test_size= 0.20\n",
    "seed = 10\n",
    "X_train, X_test, Y_train, Y_test= train_test_split(X, Y, test_size=test_size, random_state=seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('CART', DecisionTreeRegressor()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "results_e2 = []\n",
    "names_e2 = []\n",
    "\n",
    "for name, model in models:\n",
    "    # define how to split off validation data\n",
    "    kfold = KFold(n_splits=10, random_state=seed)\n",
    "    \n",
    "    # train the model\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='neg_mean_squared_error')\n",
    "    results_e2.append(cv_results)\n",
    "    names_e2.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results_e2)\n",
    "\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax.set_xticklabels(names_e2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = LogisticRegression()\n",
    "LR.fit(X_train, Y_train)\n",
    "\n",
    "predictions = LR.predict(X_test)\n",
    "\n",
    "print(accuracy_score(Y_test, predictions))\n",
    "# Confusion Matrix \n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pylab as pl\n",
    "\n",
    "print(confusion_matrix(Y_test, predictions))\n",
    "\n",
    "cm = confusion_matrix(Y_test, predictions)\n",
    "pl.matshow(cm)\n",
    "pl.title('Confusion matrix of the classifier')\n",
    "pl.colorbar()\n",
    "pl.show()\n",
    "\n",
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Main objective of the portuguse bank project was to increase the effectiveness of the telemarketing campaign. This was successful through our data analysis. The target customer profile was established while classification/regression models were built to predict customers response for term deposit in the bank\n",
    "\n",
    "The most responsive customers possess these features:\n",
    "\n",
    "   Feature A: age < 30 or age > 60\n",
    "   Feature B: a balance of more than 5000 euros\n",
    "  \n",
    "  By Logisitic algorithm, the bank will be able to predict the customer response to telemarketing campaign before calling its customer. Bank will have to call only to those customers who are more likely to get the term deposits & make less call/ignore to the customers who are unlikely to make the deposits\n",
    "  When implementing the startegy, the time of calling should be cosnidered as the analysis had shown that the calls in few months had more success rate. Also we cant judge the result by few data, we might need to analyse huge amount of data for better results\n",
    "  "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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