prompt_III-OS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Application III: Comparing Classifiers\n",
    "\n",
    "**Overview**: In this practical application, your goal is to compare the performance of the classifiers we encountered in this section, namely K Nearest Neighbor, Logistic Regression, Decision Trees, and Support Vector Machines.  We will utilize a dataset related to marketing bank products over the telephone.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Started\n",
    "\n",
    "Our dataset comes from the UCI Machine Learning repository [link](https://archive.ics.uci.edu/ml/datasets/bank+marketing).  The data is from a Portugese banking institution and is a collection of the results of multiple marketing campaigns.  We will make use of the article accompanying the dataset [here](CRISP-DM-BANK.pdf) for more information on the data and features.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1: Understanding the Data\n",
    "\n",
    "To gain a better understanding of the data, please read the information provided in the UCI link above, and examine the **Materials and Methods** section of the paper.  How many marketing campaigns does this data represent?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*17*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2: Read in the Data\n",
    "\n",
    "Use pandas to read in the dataset `bank-additional-full.csv` and assign to a meaningful variable name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, accuracy_score,  ConfusionMatrixDisplay\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from time import process_time\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/bank-additional-full.csv', sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
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       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
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       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
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       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
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       "      <td>1</td>\n",
       "      <td>999</td>\n",
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       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: Understanding the Features\n",
    "\n",
    "\n",
    "Examine the data description below, and determine if any of the features are missing values or need to be coerced to a different data type.\n",
    "\n",
    "\n",
    "```\n",
    "Input variables:\n",
    "# bank client data:\n",
    "1 - age (numeric)\n",
    "2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "5 - default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "6 - housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "7 - loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "# related with the last contact of the current campaign:\n",
    "8 - contact: contact communication type (categorical: 'cellular','telephone')\n",
    "9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "\n",
    "# other attributes:\n",
    "12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "14 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "# social and economic context attributes\n",
    "16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "17 - cons.price.idx: consumer price index - monthly indicator (numeric)\n",
    "18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)\n",
    "19 - euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "20 - nr.employed: number of employees - quarterly indicator (numeric)\n",
    "\n",
    "Output variable (desired target):\n",
    "21 - y - has the client subscribed a term deposit? (binary: 'yes','no')\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The dataset is not missing values but there are several catagorial featrues that will need to be encoded.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4: Understanding the Task\n",
    "\n",
    "After examining the description and data, your goal now is to clearly state the *Business Objective* of the task.  State the objective below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The business goal is to increase long-term deposits of the bank using a targeted marketing canpaign.  The approach is to use data mining (DM)techniques to find patterns that identify the characteristics of customers who buy the banks offers and DM is being used to increase the precision of that outreach, with the hope of reducing the nubmer of contacts made, but keeping the number of new deposits the same, thus increasing efficiency and reducing the cost of the campaign.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 5: Engineering Features\n",
    "\n",
    "Now that you understand your business objective, we will build a basic model to get started.  Before we can do this, we must work to encode the data.  Using just the bank information features (columns 1 - 7), prepare the features and target column for modeling with appropriate encoding and transformations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extact features indicated in assignment and rename target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education  default housing loan  \\\n",
       "0       56    housemaid  married             basic.4y       no      no   no   \n",
       "1       57     services  married          high.school  unknown      no   no   \n",
       "2       37     services  married          high.school       no     yes   no   \n",
       "3       40       admin.  married             basic.6y       no      no   no   \n",
       "4       56     services  married          high.school       no      no  yes   \n",
       "...    ...          ...      ...                  ...      ...     ...  ...   \n",
       "41183   73      retired  married  professional.course       no     yes   no   \n",
       "41184   46  blue-collar  married  professional.course       no      no   no   \n",
       "41185   56      retired  married    university.degree       no     yes   no   \n",
       "41186   44   technician  married  professional.course       no      no   no   \n",
       "41187   74      retired  married  professional.course       no     yes   no   \n",
       "\n",
       "         y  \n",
       "0       no  \n",
       "1       no  \n",
       "2       no  \n",
       "3       no  \n",
       "4       no  \n",
       "...    ...  \n",
       "41183  yes  \n",
       "41184   no  \n",
       "41185   no  \n",
       "41186  yes  \n",
       "41187   no  \n",
       "\n",
       "[41188 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.rename(columns={'y': 'deposit'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>no</td>\n",
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       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
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       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan deposit\n",
       "0   56  housemaid  married     basic.4y       no      no   no      no\n",
       "1   57   services  married  high.school  unknown      no   no      no\n",
       "2   37   services  married  high.school       no     yes   no      no\n",
       "3   40     admin.  married     basic.6y       no      no   no      no\n",
       "4   56   services  married  high.school       no      no  yes      no"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert target values to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['deposit'] = df2['deposit'].map({'yes': 1, 'no': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>8028</th>\n",
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       "      <td>basic.4y</td>\n",
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       "      <th>33507</th>\n",
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       "      <td>services</td>\n",
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       "      <td>no</td>\n",
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       "      <th>38452</th>\n",
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       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27054</th>\n",
       "      <td>48</td>\n",
       "      <td>management</td>\n",
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       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28833</th>\n",
       "      <td>44</td>\n",
       "      <td>admin.</td>\n",
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       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <th>24616</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>0</td>\n",
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       "      <th>37570</th>\n",
       "      <td>70</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12648</th>\n",
       "      <td>35</td>\n",
       "      <td>blue-collar</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33624</th>\n",
       "      <td>26</td>\n",
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       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       age            job   marital            education  default  housing  \\\n",
       "36706   52         admin.   married    university.degree       no      yes   \n",
       "36608   36     management   married    university.degree       no       no   \n",
       "11933   56     technician   married              unknown       no       no   \n",
       "22033   31     technician   married    university.degree  unknown       no   \n",
       "17921   54   entrepreneur  divorced          high.school  unknown       no   \n",
       "24459   46         admin.   married    university.degree  unknown       no   \n",
       "3874    35    blue-collar   married             basic.4y       no      yes   \n",
       "16276   48   entrepreneur    single    university.degree       no       no   \n",
       "23995   58        retired   married             basic.4y       no      yes   \n",
       "19133   58     technician   married  professional.course  unknown       no   \n",
       "8028    47      housemaid   married             basic.4y       no      yes   \n",
       "33507   32       services   married          high.school       no       no   \n",
       "8069    35         admin.   married          high.school       no      yes   \n",
       "11201   41         admin.  divorced          high.school       no  unknown   \n",
       "16346   35       services   married              unknown  unknown      yes   \n",
       "20059   56        unknown   married             basic.4y       no      yes   \n",
       "30104   62  self-employed   married    university.degree       no       no   \n",
       "30804   44   entrepreneur   married             basic.4y       no  unknown   \n",
       "38452   98        retired   married             basic.4y  unknown      yes   \n",
       "27054   48     management   married    university.degree       no       no   \n",
       "28833   44         admin.    single          high.school       no      yes   \n",
       "24616   32    blue-collar    single    university.degree       no       no   \n",
       "37570   70        retired   married  professional.course       no      yes   \n",
       "12648   35    blue-collar    single              unknown       no      yes   \n",
       "33624   26        student    single          high.school       no       no   \n",
       "\n",
       "          loan  deposit  \n",
       "36706       no        0  \n",
       "36608       no        1  \n",
       "11933      yes        1  \n",
       "22033       no        0  \n",
       "17921      yes        0  \n",
       "24459       no        0  \n",
       "3874        no        0  \n",
       "16276      yes        0  \n",
       "23995      yes        0  \n",
       "19133       no        0  \n",
       "8028       yes        0  \n",
       "33507       no        1  \n",
       "8069       yes        0  \n",
       "11201  unknown        0  \n",
       "16346      yes        0  \n",
       "20059       no        0  \n",
       "30104       no        0  \n",
       "30804  unknown        0  \n",
       "38452       no        1  \n",
       "27054      yes        1  \n",
       "28833       no        0  \n",
       "24616       no        0  \n",
       "37570       no        1  \n",
       "12648       no        0  \n",
       "33624       no        0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample(n=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode catagorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features = ['job', 'marital', 'education', 'default', 'housing', 'loan']\n",
    "cat_features_df = pd.DataFrame(df2[cat_features])\n",
    "dummies = pd.get_dummies(cat_features_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_enc = pd.concat([df2, dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_enc.drop(cat_features, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Move target to end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_cols = df_enc.columns.tolist()\n",
    "index = df_enc.columns.get_loc('deposit')\n",
    "new_cols = temp_cols[0:index] + temp_cols[index+1:] + temp_cols[index:index+1] \n",
    "df_enc = df_enc[new_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job_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>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_services</th>\n",
       "      <th>job_student</th>\n",
       "      <th>...</th>\n",
       "      <th>default_no</th>\n",
       "      <th>default_unknown</th>\n",
       "      <th>default_yes</th>\n",
       "      <th>housing_no</th>\n",
       "      <th>housing_unknown</th>\n",
       "      <th>housing_yes</th>\n",
       "      <th>loan_no</th>\n",
       "      <th>loan_unknown</th>\n",
       "      <th>loan_yes</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</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>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job_admin.  job_blue-collar  job_entrepreneur  job_housemaid  \\\n",
       "0   56           0                0                 0              1   \n",
       "1   57           0                0                 0              0   \n",
       "2   37           0                0                 0              0   \n",
       "3   40           1                0                 0              0   \n",
       "4   56           0                0                 0              0   \n",
       "\n",
       "   job_management  job_retired  job_self-employed  job_services  job_student  \\\n",
       "0               0            0                  0             0            0   \n",
       "1               0            0                  0             1            0   \n",
       "2               0            0                  0             1            0   \n",
       "3               0            0                  0             0            0   \n",
       "4               0            0                  0             1            0   \n",
       "\n",
       "   ...  default_no  default_unknown  default_yes  housing_no  housing_unknown  \\\n",
       "0  ...           1                0            0           1                0   \n",
       "1  ...           0                1            0           1                0   \n",
       "2  ...           1                0            0           0                0   \n",
       "3  ...           1                0            0           1                0   \n",
       "4  ...           1                0            0           1                0   \n",
       "\n",
       "   housing_yes  loan_no  loan_unknown  loan_yes  deposit  \n",
       "0            0        1             0         0        0  \n",
       "1            0        1             0         0        0  \n",
       "2            1        1             0         0        0  \n",
       "3            0        1             0         0        0  \n",
       "4            0        0             0         1        0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 35 columns):\n",
      " #   Column                         Non-Null Count  Dtype\n",
      "---  ------                         --------------  -----\n",
      " 0   age                            41188 non-null  int64\n",
      " 1   job_admin.                     41188 non-null  uint8\n",
      " 2   job_blue-collar                41188 non-null  uint8\n",
      " 3   job_entrepreneur               41188 non-null  uint8\n",
      " 4   job_housemaid                  41188 non-null  uint8\n",
      " 5   job_management                 41188 non-null  uint8\n",
      " 6   job_retired                    41188 non-null  uint8\n",
      " 7   job_self-employed              41188 non-null  uint8\n",
      " 8   job_services                   41188 non-null  uint8\n",
      " 9   job_student                    41188 non-null  uint8\n",
      " 10  job_technician                 41188 non-null  uint8\n",
      " 11  job_unemployed                 41188 non-null  uint8\n",
      " 12  job_unknown                    41188 non-null  uint8\n",
      " 13  marital_divorced               41188 non-null  uint8\n",
      " 14  marital_married                41188 non-null  uint8\n",
      " 15  marital_single                 41188 non-null  uint8\n",
      " 16  marital_unknown                41188 non-null  uint8\n",
      " 17  education_basic.4y             41188 non-null  uint8\n",
      " 18  education_basic.6y             41188 non-null  uint8\n",
      " 19  education_basic.9y             41188 non-null  uint8\n",
      " 20  education_high.school          41188 non-null  uint8\n",
      " 21  education_illiterate           41188 non-null  uint8\n",
      " 22  education_professional.course  41188 non-null  uint8\n",
      " 23  education_university.degree    41188 non-null  uint8\n",
      " 24  education_unknown              41188 non-null  uint8\n",
      " 25  default_no                     41188 non-null  uint8\n",
      " 26  default_unknown                41188 non-null  uint8\n",
      " 27  default_yes                    41188 non-null  uint8\n",
      " 28  housing_no                     41188 non-null  uint8\n",
      " 29  housing_unknown                41188 non-null  uint8\n",
      " 30  housing_yes                    41188 non-null  uint8\n",
      " 31  loan_no                        41188 non-null  uint8\n",
      " 32  loan_unknown                   41188 non-null  uint8\n",
      " 33  loan_yes                       41188 non-null  uint8\n",
      " 34  deposit                        41188 non-null  int64\n",
      "dtypes: int64(2), uint8(33)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_enc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: deposit, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc['deposit'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "for column in cat_features:\n",
    "    plt.figure(figsize=(20,4))\n",
    "    plt.subplot(121)\n",
    "    df2[column].value_counts().plot(kind='bar')\n",
    "    plt.title(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,15))\n",
    "sns.heatmap(df_enc.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One interesting thing to be gleaned from this heatmap is the the 'housing' and 'loan' features don't apprear to be all that important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Decision \n",
    "\n",
    "for column in cat_features:\n",
    "    plt.figure(figsize=(20,4))\n",
    "    plt.subplot(121)\n",
    "    sns.countplot(x=df2[column],hue=df2['deposit'],data=df2)\n",
    "    plt.title(column)    \n",
    "    plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for intersting features, we can see the patterns in job(admin, technician, and blue-collar), education (university degree), not being in default and not having a loan.  Housing and marital status don't appear to have much differentiation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 6: Train/Test Split\n",
    "\n",
    "With your data prepared, split it into a train and test set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split out target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_enc.drop(['deposit'], axis=1).values\n",
    "y = df_enc['deposit'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a smaller feature set based on the EDA observation, focusing on job, education and loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = df_enc.drop(['marital_divorced','marital_married','marital_single', 'housing_no', 'housing_unknown','housing_yes','loan_no','loan_unknown', 'loan_yes', 'deposit'], axis=1).values\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 7: A Baseline Model\n",
    "\n",
    "Before we build our first model, we want to establish a baseline.  What is the baseline performance that our classifier should aim to beat?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a baseline, going to uyse a simple linear regression using a subset of features.  Based on the bivaraite graphs, going to use job, education and default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_clf = \"\"\n",
    "baseline_score = \"\"\n",
    "\n",
    "baseline_clf = DummyClassifier()\n",
    "t0 = process_time()\n",
    "baseline_clf.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "baseline_time = t1 - t0\n",
    "baseline_score = baseline_clf.score(X2_test, y2_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Score: 88.803\n"
     ]
    }
   ],
   "source": [
    "print('Baseline Score: %.3f' % baseline_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 8: A Simple Model\n",
    "\n",
    "Use Logistic Regression to build a basic model on your data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgr = LogisticRegression()\n",
    "t0 = process_time()\n",
    "lgr.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "lgr_time = t1 - t0\n",
    "\n",
    "lgr_preds_train = lgr.predict(X2_train)\n",
    "lgr_preds_test = lgr.predict(X2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 9: Score the Model\n",
    "\n",
    "What is the accuracy of your model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression training accuracy: 88.712\n",
      "Logistic Regression test accuracy: 88.803\n"
     ]
    }
   ],
   "source": [
    "lgr_acc_train = metrics.accuracy_score(y2_train, lgr_preds_train) * 100\n",
    "print('Logistic Regression training accuracy: %.3f' % lgr_acc_train)\n",
    "lgr_acc_test = metrics.accuracy_score(y2_test, lgr_preds_test) * 100\n",
    "print('Logistic Regression test accuracy: %.3f' % lgr_acc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgr_confusion_mat = confusion_matrix(y2_test, lgr_preds_test, labels=[1,0])\n",
    "lgr_disp = ConfusionMatrixDisplay(confusion_matrix=lgr_confusion_mat, display_labels=['Yes', 'No' ])   \n",
    "lgr_disp.plot()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 10: Model Comparisons\n",
    "\n",
    "Now, we aim to compare the performance of the Logistic Regression model to our KNN algorithm, Decision Tree, and SVM models.  Using the default settings for each of the models, fit and score each.  Also, be sure to compare the fit time of each of the models.  Present your findings in a `DataFrame` similar to that below:\n",
    "\n",
    "| Model | Train Time | Train Accuracy | Test Accuracy |\n",
    "| ----- | ---------- | -------------  | -----------   |\n",
    "|     |    |.     |.     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN training accuracy: 88.997\n",
      "KNN test accuracy: 88.356\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier() \n",
    "t0 = process_time()\n",
    "knn.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "knn_time = t1 - t0\n",
    "\n",
    "knn_preds_train = knn.predict(X2_train)\n",
    "knn_preds_test = knn.predict(X2_test)\n",
    "knn_acc_train = metrics.accuracy_score(y2_train, knn_preds_train) * 100\n",
    "print('KNN training accuracy: %.3f' % knn_acc_train)\n",
    "knn_acc_test = metrics.accuracy_score(y2_test, knn_preds_test) * 100\n",
    "print('KNN test accuracy: %.3f' % knn_acc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_confusion_mat = confusion_matrix(y2_test, knn_preds_test, labels=[1,0])\n",
    "knn_disp = ConfusionMatrixDisplay(confusion_matrix=knn_confusion_mat, display_labels=['Yes', 'No' ])   \n",
    "knn_disp.plot()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM training accuracy: 88.712\n",
      "SVM test accuracy: 88.803\n"
     ]
    }
   ],
   "source": [
    "svm = SVC() \n",
    "t0 = process_time()\n",
    "svm.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "svm_time = t1 - t0\n",
    "\n",
    "svm_preds_train = svm.predict(X2_train)\n",
    "svm_preds_test = svm.predict(X2_test)\n",
    "svm_acc_train = metrics.accuracy_score(y2_train, svm_preds_train) * 100\n",
    "print('SVM training accuracy: %.3f' % svm_acc_train)\n",
    "svm_acc_test = metrics.accuracy_score(y2_test, svm_preds_test) * 100\n",
    "print('SVM test accuracy: %.3f' % svm_acc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_confusion_mat = confusion_matrix(y2_test, svm_preds_test, labels=[1,0])\n",
    "svm_disp = ConfusionMatrixDisplay(confusion_matrix=svm_confusion_mat, display_labels=['Yes', 'No' ])   \n",
    "svm_disp.plot()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree training accuracy: 89.641\n",
      "Decision Tree test accuracy: 88.550\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier() \n",
    "t0 = process_time()\n",
    "dt.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "dt_time = t1 - t0\n",
    "\n",
    "dt_preds_train = dt.predict(X2_train)\n",
    "dt_preds_test = dt.predict(X2_test)\n",
    "dt_acc_train = metrics.accuracy_score(y2_train, dt_preds_train) * 100\n",
    "print('Decision Tree training accuracy: %.3f' % dt_acc_train)\n",
    "dt_acc_test = metrics.accuracy_score(y2_test, dt_preds_test) * 100\n",
    "print('Decision Tree test accuracy: %.3f' % dt_acc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_confusion_mat = confusion_matrix(y2_test, dt_preds_test, labels=[1,0])\n",
    "dt_disp = ConfusionMatrixDisplay(confusion_matrix=svm_confusion_mat, display_labels=['Yes', 'No' ])   \n",
    "dt_disp.plot()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_data = [['Logistic Regression', lgr_acc_train, lgr_acc_test, lgr_time],\n",
    "                ['KNN', knn_acc_train, knn_acc_test, knn_time],\n",
    "                ['SVM', svm_acc_train, svm_acc_test, svm_time],\n",
    "                ['Decision Tree', dt_acc_train, dt_acc_test, dt_time]]\n",
    "results_df = pd.DataFrame(results_data, columns=['Model', 'Train Acc', 'Test Acc', 'Fit Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Model</th>\n",
       "      <th>Train Acc</th>\n",
       "      <th>Test Acc</th>\n",
       "      <th>Fit Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>88.711923</td>\n",
       "      <td>88.802564</td>\n",
       "      <td>2.059263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>88.996795</td>\n",
       "      <td>88.355832</td>\n",
       "      <td>0.004277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>88.711923</td>\n",
       "      <td>88.802564</td>\n",
       "      <td>3.474285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>89.640996</td>\n",
       "      <td>88.550063</td>\n",
       "      <td>0.066922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Train Acc   Test Acc  Fit Time\n",
       "0  Logistic Regression  88.711923  88.802564  2.059263\n",
       "1                  KNN  88.996795  88.355832  0.004277\n",
       "2                  SVM  88.711923  88.802564  3.474285\n",
       "3        Decision Tree  89.640996  88.550063  0.066922"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While SVM and Logistic Regression have the highest test accuracy, they are also the most compute intensive.  On the other hand, the Decision Tree has almost as high test accuracy at significantly lower compute cost.  That would be my recommendation at that seems to strike the best balance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 11: Improving the Model\n",
    "\n",
    "Now that we have some basic models on the board, we want to try to improve these.  Below, we list a few things to explore in this pursuit.\n",
    "\n",
    "- More feature engineering and exploration.  For example, should we keep the gender feature?  Why or why not?\n",
    "- Hyperparameter tuning and grid search.  All of our models have additional hyperparameters to tune and explore.  For example the number of neighbors in KNN or the maximum depth of a Decision Tree.  \n",
    "- Adjust your performance metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the decision tree with the full set of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree training accuracy: 91.697\n",
      "Decision Tree test accuracy: 86.433\n",
      "Decision Tree fit time: 0.09774\n"
     ]
    }
   ],
   "source": [
    "dt_full = DecisionTreeClassifier() \n",
    "t0 = process_time()\n",
    "dt_full.fit(X_train, y_train)\n",
    "t1 = process_time()\n",
    "dt_full_time = t1 - t0\n",
    "\n",
    "dt_full_preds_train = dt_full.predict(X_train)\n",
    "dt_full_preds_test = dt_full.predict(X_test)\n",
    "dt_full_acc_train = metrics.accuracy_score(y_train, dt_full_preds_train) * 100\n",
    "print('Decision Tree training accuracy: %.3f' % dt_full_acc_train)\n",
    "dt_full_acc_test = metrics.accuracy_score(y_test, dt_full_preds_test) * 100\n",
    "print('Decision Tree test accuracy: %.3f' % dt_full_acc_test)\n",
    "print('Decision Tree fit time: %.5f' % dt_full_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While training accuracy went up, the test accuracy actually went down, which liely means the model is now overfit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let play with the depth of the original tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The depth of the original tree is:  34\n"
     ]
    }
   ],
   "source": [
    "print('The depth of the original tree is: ', dt.tree_.max_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree (depth=none) training accuracy: 89.641\n",
      "Decision Tree (depth=none) test accuracy: 88.550\n",
      "Decision Tree (depth=none) fit time: 0.07166\n"
     ]
    }
   ],
   "source": [
    "dt_none = DecisionTreeClassifier(max_depth=None) \n",
    "t0 = process_time()\n",
    "dt_none.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "dt_none_time = t1 - t0\n",
    "\n",
    "dt_none_preds_train = dt_none.predict(X2_train)\n",
    "dt_none_preds_test = dt_none.predict(X2_test)\n",
    "dt_none_acc_train = metrics.accuracy_score(y2_train, dt_none_preds_train) * 100\n",
    "print('Decision Tree (depth=none) training accuracy: %.3f' % dt_none_acc_train)\n",
    "dt_none_acc_test = metrics.accuracy_score(y2_test, dt_none_preds_test) * 100\n",
    "print('Decision Tree (depth=none) test accuracy: %.3f' % dt_none_acc_test)\n",
    "print('Decision Tree (depth=none) fit time: %.5f' % dt_none_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree (depth=5) training accuracy: 88.786\n",
      "Decision Tree (depth=5) test accuracy: 88.764\n",
      "Decision Tree (depth=5) fit time: 0.04079\n"
     ]
    }
   ],
   "source": [
    "dt_5 = DecisionTreeClassifier(max_depth=5) \n",
    "t0 = process_time()\n",
    "dt_5.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "dt_5_time = t1 - t0\n",
    "\n",
    "dt_5_preds_train = dt_5.predict(X2_train)\n",
    "dt_5_preds_test = dt_5.predict(X2_test)\n",
    "dt_5_acc_train = metrics.accuracy_score(y2_train, dt_5_preds_train) * 100\n",
    "print('Decision Tree (depth=5) training accuracy: %.3f' % dt_5_acc_train)\n",
    "dt_5_acc_test = metrics.accuracy_score(y2_test, dt_5_preds_test) * 100\n",
    "print('Decision Tree (depth=5) test accuracy: %.3f' % dt_5_acc_test)\n",
    "print('Decision Tree (depth=5) fit time: %.5f' % dt_5_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree (depth=10) training accuracy: 89.126\n",
      "Decision Tree (depth=10) test accuracy: 88.744\n",
      "Decision Tree (depth=10) fit time: 0.04893\n"
     ]
    }
   ],
   "source": [
    "dt_10 = DecisionTreeClassifier(max_depth=10) \n",
    "t0 = process_time()\n",
    "dt_10.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "dt_10_time = t1 - t0\n",
    "\n",
    "dt_10_preds_train = dt_10.predict(X2_train)\n",
    "dt_10_preds_test = dt_10.predict(X2_test)\n",
    "dt_10_acc_train = metrics.accuracy_score(y2_train, dt_10_preds_train) * 100\n",
    "print('Decision Tree (depth=10) training accuracy: %.3f' % dt_10_acc_train)\n",
    "dt_10_acc_test = metrics.accuracy_score(y2_test, dt_10_preds_test) * 100\n",
    "print('Decision Tree (depth=10) test accuracy: %.3f' % dt_10_acc_test)\n",
    "print('Decision Tree (depth=10) fit time: %.5f' % dt_10_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree (depth=3) training accuracy: 88.715\n",
      "Decision Tree (depth=3) test accuracy: 88.764\n",
      "Decision Tree (depth=3) fit time: 0.02632\n"
     ]
    }
   ],
   "source": [
    "dt_3 = DecisionTreeClassifier(max_depth=3) \n",
    "t0 = process_time()\n",
    "dt_3.fit(X2_train, y2_train)\n",
    "t1 = process_time()\n",
    "dt_3_time = t1 - t0\n",
    "\n",
    "dt_3_preds_train = dt_3.predict(X2_train)\n",
    "dt_3_preds_test = dt_3.predict(X2_test)\n",
    "dt_3_acc_train = metrics.accuracy_score(y2_train, dt_3_preds_train) * 100\n",
    "print('Decision Tree (depth=3) training accuracy: %.3f' % dt_3_acc_train)\n",
    "dt_3_acc_test = metrics.accuracy_score(y2_test, dt_5_preds_test) * 100\n",
    "print('Decision Tree (depth=3) test accuracy: %.3f' % dt_3_acc_test)\n",
    "print('Decision Tree (depth=3) fit time: %.5f' % dt_3_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the tree depth down to 3 has neglibile impact on accuracy but greatly reduces compute time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_3_confusion_mat = confusion_matrix(y2_test, dt_3_preds_test, labels=[1,0])\n",
    "dt_3_disp = ConfusionMatrixDisplay(confusion_matrix=dt_3_confusion_mat, display_labels=['Yes', 'No' ])   \n",
    "dt_3_disp.plot()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Questions"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}