EasyVisa Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7708628a",
   "metadata": {
    "id": "AT5OogJVFbwu"
   },
   "source": [
    "# Ensemble Learning Project: EasyVisa\n",
    "\n",
    "## Context:\n",
    "\n",
    "Business communities in the United States are facing high demand for human resources, but one of the constant challenges is identifying and attracting the right talent, which is perhaps the most important element in remaining competitive. Companies in the United States look for hard-working, talented, and qualified individuals both locally as well as abroad.\n",
    "\n",
    "The Immigration and Nationality Act (INA) of the US permits foreign workers to come to the United States to work on either a temporary or permanent basis. The act also protects US workers against adverse impacts on their wages or working conditions by ensuring US employers' compliance with statutory requirements when they hire foreign workers to fill workforce shortages. The immigration programs are administered by the Office of Foreign Labor Certification (OFLC).\n",
    "\n",
    "OFLC processes job certification applications for employers seeking to bring foreign workers into the United States and grants certifications in those cases where employers can demonstrate that there are not sufficient US workers available to perform the work at wages that meet or exceed the wage paid for the occupation in the area of intended employment.\n",
    "\n",
    "## Objective:\n",
    "\n",
    "In FY 2016, the OFLC processed 775,979 employer applications for 1,699,957 positions for temporary and permanent labor certifications. This was a nine percent increase in the overall number of processed applications from the previous year. The process of reviewing every case is becoming a tedious task as the number of applicants is increasing every year.\n",
    "\n",
    "The increasing number of applicants every year calls for a Machine Learning based solution that can help in shortlisting the candidates having higher chances of VISA approval. OFLC has hired your firm EasyVisa for data-driven solutions. You as a data scientist have to analyze the data provided and, with the help of a classification model:\n",
    "\n",
    "* Facilitate the process of visa approvals.\n",
    "* Recommend a suitable profile for the applicants for whom the visa should be certified or denied based on the drivers that significantly influence the case status. \n",
    "\n",
    "\n",
    "## Data Description\n",
    "\n",
    "The data contains the different attributes of the employee and the employer. The detailed data dictionary is given below.\n",
    "\n",
    "* case_id: ID of each visa application\n",
    "* continent: Information of continent the employee\n",
    "* education_of_employee: Information of education of the employee\n",
    "* has_job_experience: Does the employee has any job experience? Y= Yes; N = No\n",
    "* requires_job_training: Does the employee require any job training? Y = Yes; N = No \n",
    "* no_of_employees: Number of employees in the employer's company\n",
    "* yr_of_estab: Year in which the employer's company was established\n",
    "* region_of_employment: Information of foreign worker's intended region of employment in the US.\n",
    "* prevailing_wage:  Average wage paid to similarly employed workers in a specific occupation in the area of intended employment. The purpose of the prevailing wage is to ensure that the foreign worker is not underpaid compared to other workers offering the same or similar service in the same area of employment. \n",
    "* unit_of_wage: Unit of prevailing wage. Values include Hourly, Weekly, Monthly, and Yearly.\n",
    "* full_time_position: Is the position of work full-time? Y = Full Time Position; N = Part Time Position\n",
    "* case_status:  Flag indicating if the Visa was certified or denied"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a955ba46",
   "metadata": {
    "id": "dirty-island"
   },
   "source": [
    "## Importing necessary libraries, creating dataframe and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70c3de0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\pneid\\anaconda3\\lib\\site-packages (1.5.1)\n",
      "Requirement already satisfied: scipy in c:\\users\\pneid\\anaconda3\\lib\\site-packages (from xgboost) (1.6.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\pneid\\anaconda3\\lib\\site-packages (from xgboost) (1.18.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "892a9f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries to read and manipulate data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#libraries to visualize data\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#libraries for model building and tuning\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import (BaggingClassifier,RandomForestClassifier, \n",
    "                              GradientBoostingClassifier, AdaBoostClassifier, StackingClassifier)\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "#libraries for metrics and statistics\n",
    "from sklearn import metrics\n",
    "import scipy.stats as stats\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score, roc_auc_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3c26262",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading csv file into a pandas Dataframe\n",
    "visa = pd.read_csv('EasyVisa.csv')\n",
    "# copying data to another varaible to preserve original data\n",
    "df = visa.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "556d0d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "#format numeric data for easier readability\n",
    "pd.set_option(\n",
    "    \"display.float_format\", lambda x: \"%.2f\" % x\n",
    ")  # to display numbers rounded off to 2 decimal places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f833406b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create labeled barplots\n",
    "\n",
    "def labeled_barplot(data, feature, feature_2, order, perc=False, n=None):\n",
    "    \"\"\"\n",
    "    Barplot with percentage at the top\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    feature_2: dataframe column\n",
    "    perc: whether to display percentages instead of count (default is False)\n",
    "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
    "    \"\"\"\n",
    "\n",
    "    total = len(data[feature])  # length of the column\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 1, 5))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 1, 5))\n",
    "        \n",
    "    ax = sns.countplot(\n",
    "        data=data,\n",
    "        x=feature,\n",
    "        palette='viridis',\n",
    "        order=order,\n",
    "        hue=feature_2,\n",
    "    )\n",
    "\n",
    "    for p in ax.patches:\n",
    "        if perc == True:\n",
    "            label = \"{:.1f}%\".format(\n",
    "                100 * p.get_height() / total\n",
    "            )  # percentage of each class of the category\n",
    "        else:\n",
    "            label = p.get_height()  # count of each level of the category\n",
    "\n",
    "        x = p.get_x() + p.get_width() / 2  # width of the plot\n",
    "        y = p.get_height()  # height of the plot\n",
    "\n",
    "        ax.annotate(\n",
    "            label,\n",
    "            (x, y),\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            size=9,\n",
    "            xytext=(0, 5),\n",
    "            textcoords=\"offset points\"\n",
    "        )  # annotate the percentage\n",
    "\n",
    "    plt.show()  # show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d001db34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plot a boxplot and a histogram along the same scale\n",
    "\n",
    "def histogram_boxplot(data, feature, figsize=(16, 6), kde=False, bins=None, hue=None):\n",
    "    \"\"\"\n",
    "    Combines boxplot and histogram\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    figsize: size of figure (default (16,6))\n",
    "    kde: whether to show the density curve (default False)\n",
    "    bins: number of bins for histogram (default None)\n",
    "    \"\"\"\n",
    "    f2, (ax_box2, ax_hist2) = plt.subplots(\n",
    "        nrows=2,  # Number of rows of the subplot grid= 2\n",
    "        sharex=True,  # x-axis will be shared among all subplots\n",
    "        gridspec_kw={\"height_ratios\": (0.25, 0.75)},\n",
    "        figsize=figsize,\n",
    "    )  # creating the 2 subplots\n",
    "    sns.boxplot(\n",
    "        data=data, x=feature, ax=ax_box2, showmeans=True,\n",
    "    )  # boxplot will be created and a star will indicate the mean value of the column\n",
    "    sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\"winter\",\n",
    "    ) if bins else sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2\n",
    "    )  # For histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].mean(), color=\"green\", linestyle=\"--\"\n",
    "    )  # Add mean to the histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].median(), color=\"black\", linestyle=\"-\"\n",
    "    )  # Add median to the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "99fa0bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plot stacked bar chart\n",
    "\n",
    "def stacked_barplot(data, predictor, target):\n",
    "    \"\"\"\n",
    "    Print the category counts and plot a stacked bar chart\n",
    "\n",
    "    data: dataframe\n",
    "    predictor: independent variable\n",
    "    target: target variable\n",
    "    \"\"\"\n",
    "    count = data[predictor].nunique()\n",
    "    sorter = data[target].value_counts().index[-1]\n",
    "    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(\n",
    "        by=sorter, ascending=False\n",
    "    )\n",
    "    print(tab1)\n",
    "    print(\"-\" * 120)\n",
    "    tab = pd.crosstab(data[predictor], data[target], normalize=\"index\").sort_values(\n",
    "        by=sorter, ascending=False\n",
    "    )\n",
    "    tab.plot(kind=\"bar\", stacked=True, figsize=(count + 1, 5), cmap='viridis')\n",
    "    plt.legend(\n",
    "        loc=\"lower left\",\n",
    "        frameon=False,\n",
    "    )\n",
    "    plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db20b80",
   "metadata": {
    "id": "desperate-infection"
   },
   "source": [
    "## Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b5309eb",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.20</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.65</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.86</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.03</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.39</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case_id continent education_of_employee has_job_experience  \\\n",
       "0  EZYV01      Asia           High School                  N   \n",
       "1  EZYV02      Asia              Master's                  Y   \n",
       "2  EZYV03      Asia            Bachelor's                  N   \n",
       "3  EZYV04      Asia            Bachelor's                  N   \n",
       "4  EZYV05    Africa              Master's                  Y   \n",
       "\n",
       "  requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "0                     N            14513         2007                 West   \n",
       "1                     N             2412         2002            Northeast   \n",
       "2                     Y            44444         2008                 West   \n",
       "3                     N               98         1897                 West   \n",
       "4                     N             1082         2005                South   \n",
       "\n",
       "   prevailing_wage unit_of_wage full_time_position case_status  \n",
       "0           592.20         Hour                  Y      Denied  \n",
       "1         83425.65         Year                  Y   Certified  \n",
       "2        122996.86         Year                  Y      Denied  \n",
       "3         83434.03         Year                  Y      Denied  \n",
       "4        149907.39         Year                  Y   Certified  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print first five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "840ceeea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.57</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.79</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.85</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.77</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.91</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "25475                South         77092.57         Year                  Y   \n",
       "25476            Northeast        279174.79         Year                  Y   \n",
       "25477                South        146298.85         Year                  N   \n",
       "25478                 West         86154.77         Year                  Y   \n",
       "25479              Midwest         70876.91         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print last five rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8348f21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25480, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print shape of dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447f0dd9",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "- Dataset has 25,480 rows and 12 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db21f738",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25480 entries, 0 to 25479\n",
      "Data columns (total 12 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   case_id                25480 non-null  object \n",
      " 1   continent              25480 non-null  object \n",
      " 2   education_of_employee  25480 non-null  object \n",
      " 3   has_job_experience     25480 non-null  object \n",
      " 4   requires_job_training  25480 non-null  object \n",
      " 5   no_of_employees        25480 non-null  int64  \n",
      " 6   yr_of_estab            25480 non-null  int64  \n",
      " 7   region_of_employment   25480 non-null  object \n",
      " 8   prevailing_wage        25480 non-null  float64\n",
      " 9   unit_of_wage           25480 non-null  object \n",
      " 10  full_time_position     25480 non-null  object \n",
      " 11  case_status            25480 non-null  object \n",
      "dtypes: float64(1), int64(2), object(9)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# print the data types of the columns within the datset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9cb1957",
   "metadata": {
    "scrolled": false
   },
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [case_id, continent, education_of_employee, has_job_experience, requires_job_training, no_of_employees, yr_of_estab, region_of_employment, prevailing_wage, unit_of_wage, full_time_position, case_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicate values\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca84d405",
   "metadata": {
    "colab_type": "text",
    "id": "X2X_FLxThU2q"
   },
   "source": [
    "* There are no missing or duplicate values in the dataset.\n",
    "* We should convert the object type columns to categories, which reduces the memory required to store the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b9312fe6",
   "metadata": {
    "id": "persistent-juice"
   },
   "outputs": [],
   "source": [
    "# convert all columns with dtype object into category\n",
    "for col in df.columns[df.dtypes=='object']:\n",
    "    df[col] = df[col].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4da297c",
   "metadata": {},
   "source": [
    "- There are 9 columns of the dtype object, 1 columns of the dtype float64, and 2 columns of the dtype int64.\n",
    "- The target variable for the models is *case_status*.\n",
    "- *case_id* is a randomly assigned by INA for each case, which should not provide any material information for model building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "239154c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the column *case_id* from the dataframe\n",
    "df.drop(labels='case_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe5d846b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25480 entries, 0 to 25479\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Non-Null Count  Dtype   \n",
      "---  ------                 --------------  -----   \n",
      " 0   continent              25480 non-null  category\n",
      " 1   education_of_employee  25480 non-null  category\n",
      " 2   has_job_experience     25480 non-null  category\n",
      " 3   requires_job_training  25480 non-null  category\n",
      " 4   no_of_employees        25480 non-null  int64   \n",
      " 5   yr_of_estab            25480 non-null  int64   \n",
      " 6   region_of_employment   25480 non-null  category\n",
      " 7   prevailing_wage        25480 non-null  float64 \n",
      " 8   unit_of_wage           25480 non-null  category\n",
      " 9   full_time_position     25480 non-null  category\n",
      " 10  case_status            25480 non-null  category\n",
      "dtypes: category(8), float64(1), int64(2)\n",
      "memory usage: 797.5 KB\n"
     ]
    }
   ],
   "source": [
    "# confirm conversions and dropped variable\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a9605f",
   "metadata": {},
   "source": [
    "* The memory required to store the dataframe is now ~800kB, a third the size of the original dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e627f87",
   "metadata": {
    "id": "seasonal-calibration"
   },
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8901e7b3",
   "metadata": {
    "id": "classified-traveler"
   },
   "source": [
    "**Leading Questions**:\n",
    "1. Those with higher education may want to travel abroad for a well-paid job. Does education play a role in Visa certification? \n",
    "\n",
    "2. How does the visa status vary across different continents? \n",
    " \n",
    "3. Experienced professionals might look abroad for opportunities to improve their lifestyles and career development. Does work experience influence visa status? \n",
    " \n",
    "4. In the United States, employees are paid at different intervals. Which pay unit is most likely to be certified for a visa? \n",
    " \n",
    "5. The US government has established a prevailing wage to protect local talent and foreign workers. How does the visa status change with the prevailing wage?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2bdf69",
   "metadata": {
    "id": "right-permit"
   },
   "source": [
    "#### 1. Those with higher education may want to travel abroad for a well-paid job. Does education play a role in Visa certification? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84ea8c25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education_of_employee  case_status\n",
       "Bachelor's             Certified      6367\n",
       "                       Denied         3867\n",
       "Doctorate              Certified      1912\n",
       "                       Denied          280\n",
       "High School            Denied         2256\n",
       "                       Certified      1164\n",
       "Master's               Certified      7575\n",
       "                       Denied         2059\n",
       "Name: case_status, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group data by education_of_employee and count the numbers of each case_status for each category\n",
    "df.groupby('education_of_employee')['case_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "95b439b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot of the data above, but additionally with the percent of each group within the total number of cases\n",
    "labeled_barplot(df,'education_of_employee','case_status', order=[\"High School\",\"Bachelor's\",\"Master's\",\"Doctorate\"], perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1243d407",
   "metadata": {},
   "source": [
    "* As the graph aboves shows, the ratio of applications being certified versus denied increases considerably as an applicant's highest level of education achieved increases.\n",
    "* The ratio of an applicant with a high school diploma being approved versus denied is ~1:2, whereas the same ratio for an applicant with a doctorate is ~7:1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01f075e",
   "metadata": {
    "id": "right-permit"
   },
   "source": [
    "#### 2. How does the visa status vary across different continents? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c42cb59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent      case_status\n",
       "Africa         Certified        397\n",
       "               Denied           154\n",
       "Asia           Certified      11012\n",
       "               Denied          5849\n",
       "Europe         Certified       2957\n",
       "               Denied           775\n",
       "North America  Certified       2037\n",
       "               Denied          1255\n",
       "Oceania        Certified        122\n",
       "               Denied            70\n",
       "South America  Certified        493\n",
       "               Denied           359\n",
       "Name: case_status, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group data by continent and count the numbers of each case_status for each category\n",
    "df.groupby('continent')['case_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c3dc249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot of the data above, but additionally with the percent of each group within the total number of cases\n",
    "labeled_barplot(df,'continent','case_status', order=None, perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b88422",
   "metadata": {},
   "source": [
    "* Applicants from Asia comprise ~2/3 of all applications and these applicants have almost a 2:1 ratio of approvals to denials.\n",
    "* An application from a European applicant has the best ratio of approvals to denials (~4:1)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3406b13d",
   "metadata": {
    "id": "right-permit"
   },
   "source": [
    "#### 3. Experienced professionals might look abroad for opportunities to improve their lifestyles and career development. Does work experience influence visa status? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99c6dd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "has_job_experience  case_status\n",
       "N                   Certified       5994\n",
       "                    Denied          4684\n",
       "Y                   Certified      11024\n",
       "                    Denied          3778\n",
       "Name: case_status, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group data by has_job_experience and count the numbers of each case_status for each category\n",
    "df.groupby('has_job_experience')['case_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a76b956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot of the data above, but additionally with the percent of each group within the total number of cases\n",
    "labeled_barplot(df,'has_job_experience','case_status', order=None, perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584aa435",
   "metadata": {},
   "source": [
    "* Applicants with job experience have a ratio of approved to denied applications of ~3:1, whereas the same ratio for applicants without job experience have around a 5:4 ratio (i.e., approximately equivalent)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35a5426",
   "metadata": {
    "id": "right-permit"
   },
   "source": [
    "#### 4. In the United States, employees are paid at different intervals. Which pay unit is most likely to be certified for a visa? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ccae0c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unit_of_wage  case_status\n",
       "Hour          Denied          1410\n",
       "              Certified        747\n",
       "Month         Certified         55\n",
       "              Denied            34\n",
       "Week          Certified        169\n",
       "              Denied           103\n",
       "Year          Certified      16047\n",
       "              Denied          6915\n",
       "Name: case_status, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group data by unit_of_wage and count the numbers of each case_status for each category\n",
    "df.groupby('unit_of_wage')['case_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1382746d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot of the data above, but additionally with the percent of each group within the total number of cases\n",
    "labeled_barplot(df,'unit_of_wage','case_status', order=None, perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c0dc54",
   "metadata": {},
   "source": [
    "* Applicants who are applying to work in a job with an hourly rate have a ratio of approved versus denied applications of ~1:2. Additionally, these applicants comprise only ~8% of all applications, but comprise ~17% of all denials. \n",
    "* Applicants from any other *unit_of_wage* category have a ratio of ~2:1, with applications for jobs with annual salaries showing nearly a 2.5:1 ratio of approvals to denials."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c581d001",
   "metadata": {
    "id": "alleged-spirituality"
   },
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "- Feature engineering \n",
    "- Outlier detection and treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d77caf54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 11)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the number of rows with a value of no_of_employees less than 0, which is not a possible value\n",
    "df[df.no_of_employees<0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6fdd355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25447, 11)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the rows with errors shown above and check the remaining number of rows\n",
    "df = df[df.no_of_employees>0]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a6fb6a8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-1000       6208\n",
       "1000-2000    5915\n",
       "2000-3000    4978\n",
       "4000+        4798\n",
       "3000-4000    3548\n",
       "Name: company_size, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create bins to categorize companies by the size of their overall workforce\n",
    "bins = [0,1000,2000,3000,4000,np.Inf]\n",
    "names = ['0-1000','1000-2000','2000-3000','3000-4000','4000+']\n",
    "#assign each applicant to a category which indicates the size of the company he or she is applying to\n",
    "df['company_size'] = pd.cut(df['no_of_employees'], bins, labels=names)\n",
    "df.company_size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "31e376af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10-20    8367\n",
       "50+      5106\n",
       "0-10     4849\n",
       "30-50    3739\n",
       "20-30    3386\n",
       "Name: company_age, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create bins to categorize companies by the number of years since the company was founded\n",
    "bins = [0,1966,1986,1996,2006,np.Inf]\n",
    "names = ['50+','30-50','20-30','10-20','0-10']\n",
    "#assign each applicant to a category which indicates how long the company has been in business (i.e., a proxy for how well-established the company is)\n",
    "df['company_age'] = pd.cut(df['yr_of_estab'], bins, labels=names)\n",
    "df.company_age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44dc093",
   "metadata": {
    "id": "difficult-union"
   },
   "source": [
    "## Follow-on EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64768850",
   "metadata": {
    "id": "right-permit"
   },
   "source": [
    "#### 5. The US government has established a prevailing wage to protect local talent and foreign workers. How does the visa status change with the prevailing wage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8bb6a48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unit_of_wage\n",
       "Hour      414.77\n",
       "Month   87592.86\n",
       "Week    85606.82\n",
       "Year    81237.54\n",
       "Name: prevailing_wage, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group data by unit_of_wage and calculate the mean prevailing_wage for each category\n",
    "df.groupby('unit_of_wage')['prevailing_wage'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd29033",
   "metadata": {},
   "source": [
    "* The data shows that the *unit_of_wage* 'Hour' is a per-day amount, whearas the other three *unit_of_wage* categories are an annual salary amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f42760a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a dataframe only of workers applying for jobs with a *unit_wage* of 'Hour'\n",
    "df_hourly = df[df.unit_of_wage=='Hour']\n",
    "#show a distribution of the *prevailing_wage* within this dataframe, along with the ratio of *case_status* within each bin\n",
    "sns.histplot(data=df_hourly, x='prevailing_wage', hue='case_status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "33fc8b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_per_day  case_status\n",
       "(0, 200]        Denied         446\n",
       "                Certified      219\n",
       "(200, 400]      Denied         300\n",
       "                Certified      166\n",
       "(400, 600]      Denied         274\n",
       "                Certified      144\n",
       "(600, 800]      Denied         238\n",
       "                Certified      116\n",
       "(800, 1000]     Denied         148\n",
       "                Certified      102\n",
       "Name: case_status, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create bins that categorize applicants by the per-day wage of the job he or she is applying for\n",
    "bins = list(np.arange(0,1100,200))\n",
    "#assign each applicant to a category\n",
    "df_hourly['income_per_day'] = pd.cut(df_hourly['prevailing_wage'], bins)\n",
    "df_hourly.groupby('income_per_day')['case_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d80c54a4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot of the data above, but additionally with the percent of each group within the total number of cases\n",
    "labeled_barplot(df_hourly,'income_per_day','case_status',perc=True,order=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12d4571",
   "metadata": {},
   "source": [
    "* As the graphs above show, even within applicants for jobs with an 'Hour' *prevailing_wage*, the ratio of approved to denied applications increases considerably as *prevailing_wage* increases.\n",
    "* Even at the highest *income_per_day* category, an application is still more likely to be denied than approved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f5dca141",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a new dataframe of only those applicants applying for jobs with annual salaries (i.e., not 'Hour')\n",
    "df_annual = df[df.unit_of_wage != \"Hour\"]\n",
    "#show the distribution of annual salaries, along with ratio of *case_status* within each bin\n",
    "plt.figure(figsize=(16,6))\n",
    "sns.histplot(data=df_annual, x='prevailing_wage', hue='case_status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d2a914",
   "metadata": {},
   "source": [
    "* The distribution of wages is (as expected) right skewed, indicating a long-tail of jobs with annual salaries above 150,000 dollars.\n",
    "* As the distribution above shows, the ratio of approved to denied applications remains approximately equivalent across the range of wages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98fa4d53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_per_year  case_status\n",
       "$0-25k           Certified      2089\n",
       "                 Denied          759\n",
       "$25k-50k         Certified      2835\n",
       "                 Denied         1232\n",
       "$50k-75k         Certified      3132\n",
       "                 Denied         1371\n",
       "$75k-100k        Certified      3011\n",
       "                 Denied         1315\n",
       "$100k-125k       Certified      2405\n",
       "                 Denied         1138\n",
       "$125k+           Certified      2782\n",
       "                 Denied         1225\n",
       "Name: case_status, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create bins to categorize a job by its annual salary, which indicates how well paying the job is\n",
    "bins = [0,25000,50000,75000,100000,125000,np.inf]\n",
    "names = ['$0-25k','$25k-50k','$50k-75k','$75k-100k','$100k-125k','$125k+']\n",
    "#assign an applicant to a category\n",
    "df_annual['income_per_year'] = pd.cut(df_annual['prevailing_wage'], bins, labels=names)\n",
    "df_annual.groupby('income_per_year')['case_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ffe46ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a plot of the data above, but additionally with the percent of each group within the total number of cases\n",
    "labeled_barplot(df_annual,'income_per_year','case_status',perc=True,order=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fba1c5",
   "metadata": {},
   "source": [
    "* In support of the observation above, there does not appear to be a significant change in ratio of approved to denied applications as *income_per_year* increases. To the contrary, the ratio may actually decrease as this *prevailing_wage* increases.\n",
    "* For applications for jobs with an *income_per_year* wage, the highest concentration of applications is for jobs with 50,000 to 75,000 dollars per year in salary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9939951",
   "metadata": {},
   "source": [
    "### Summary of int64 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99f185f8",
   "metadata": {
    "scrolled": false
   },
   "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>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>no_of_employees</th>\n",
       "      <td>25447.00</td>\n",
       "      <td>5674.42</td>\n",
       "      <td>22891.84</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1025.00</td>\n",
       "      <td>2112.00</td>\n",
       "      <td>3506.50</td>\n",
       "      <td>602069.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_of_estab</th>\n",
       "      <td>25447.00</td>\n",
       "      <td>1979.39</td>\n",
       "      <td>42.39</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>1976.00</td>\n",
       "      <td>1997.00</td>\n",
       "      <td>2005.00</td>\n",
       "      <td>2016.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count    mean      std     min     25%     50%     75%  \\\n",
       "no_of_employees 25447.00 5674.42 22891.84   12.00 1025.00 2112.00 3506.50   \n",
       "yr_of_estab     25447.00 1979.39    42.39 1800.00 1976.00 1997.00 2005.00   \n",
       "\n",
       "                      max  \n",
       "no_of_employees 602069.00  \n",
       "yr_of_estab       2016.00  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#provide summary statistics of all float64 variables\n",
    "df.describe(include = ['int64']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d01dee8e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#provide histogram and boxplots\n",
    "histogram_boxplot(df, 'no_of_employees')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c7e69e",
   "metadata": {},
   "source": [
    "* The data and graphs above, indicate the distribution of companies by number of employee is heavily right skewed, but without any outlier values that require further investigation or treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b537dae9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_boxplot(df, 'yr_of_estab')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381ec58a",
   "metadata": {},
   "source": [
    "* The data and graphs above, indicate the distribution of companies by year of establishment (i.e., founding) is heavily left skewed, but without any outlier values which require further investigation or treatment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79acb5b0",
   "metadata": {},
   "source": [
    "### Summary of float64 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "277897c8",
   "metadata": {
    "scrolled": false
   },
   "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>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>prevailing_wage</th>\n",
       "      <td>23294.00</td>\n",
       "      <td>81312.84</td>\n",
       "      <td>49943.37</td>\n",
       "      <td>100.00</td>\n",
       "      <td>43860.93</td>\n",
       "      <td>76257.68</td>\n",
       "      <td>111414.45</td>\n",
       "      <td>319210.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count     mean      std    min      25%      50%       75%  \\\n",
       "prevailing_wage 23294.00 81312.84 49943.37 100.00 43860.93 76257.68 111414.45   \n",
       "\n",
       "                      max  \n",
       "prevailing_wage 319210.27  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#provide summary statistics of all float64 variables\n",
    "df_annual.describe(include = ['float64']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f354e772",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#provide histogram and boxplot\n",
    "histogram_boxplot(df_annual,'prevailing_wage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e2a3c521",
   "metadata": {
    "scrolled": false
   },
   "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>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>prevailing_wage</th>\n",
       "      <td>2153.00</td>\n",
       "      <td>414.77</td>\n",
       "      <td>275.05</td>\n",
       "      <td>2.14</td>\n",
       "      <td>152.70</td>\n",
       "      <td>373.20</td>\n",
       "      <td>637.31</td>\n",
       "      <td>999.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count   mean    std  min    25%    50%    75%    max\n",
       "prevailing_wage 2153.00 414.77 275.05 2.14 152.70 373.20 637.31 999.92"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#provide summary statistics of all float64 variables\n",
    "df_hourly.describe(include = ['float64']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39b1b8de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#provide histogram and boxplot\n",
    "histogram_boxplot(df_hourly,'prevailing_wage')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f455603",
   "metadata": {},
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ad6b4c56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent    Africa   Asia  Europe  North America  Oceania  South America  \\\n",
      "case_status                                                                 \n",
      "All             550  16840    3727           3287      192            851   \n",
      "Certified       396  11001    2953           2037      122            492   \n",
      "Denied          154   5839     774           1250       70            359   \n",
      "\n",
      "continent      All  \n",
      "case_status         \n",
      "All          25447  \n",
      "Certified    17001  \n",
      "Denied        8446  \n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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fsmRJ42vKyspM5eXlJh8fn/rAwMC648ePe3/xxRfdrue9NHAq+EQkSUQOishhEVno4PkQEfm7iOSIyH4Ruac1xRCRc1544YXSc+fONY7Y0tLS/pmRkdHTbDZHvf/++z3eeOON445el5KScm7Tpk0B9l9utGTt2rU9kpOTr5ihZfLkyeUZGRmBzta7YsWK4/v27etiNpujwsLCopctWxbU0mteeeWVk+fOnTOFh4dHR0RERG3evNl/1KhRF2NiYi6Eh4dHT58+PTQhIcFhwLdErjUrKwCIiAmABcBEACcAfA1gqqoW2rVZASBHVdNEJArAZlUNvdZ2ExMTNTs7uzU1Nyvsg5ddur22cuSBF9xdglOin/uju0twSsEr8126PRHZq6qJ9utyc3OPxsXFOTW0pFtHbm5uz7i4uNCm653p8Y0AcFhVv1XVywBWA5jcpI0C6Gp73A2Ay8/xISJyFWe+3OgPwL7bfALAyCZt/hPANhH5FYAuAO50tCERmQ1gNgCEhIRcb60t4iVWROQMZ3p84mBd0/HxVAArVTUYwD0AMkTkqm2r6gpVTVTVxKCgFof4RERtwpngOwHA/tq5YFw9lH0UwBoAUNX/AdAZQE8QEd2CnAm+rwGEi8ggEekI4EEAHzdp808AEwBARCJhDb4yVxZKROQqLQafqtYCmAtgK4ADANaoaoGI/FZEJtmaPQXgMRHJBfA+gJna0tfFRERu4tR5fKq6WVXNqhqmqi/b1i1S1Y9tjwtVdbSqxqnqT1V1W1sWTWRUR44c6TBhwoSwgQMHxgwYMCDm3/7t3wZcunTJ0XF4lzp69GiHpKSkn7T1fm4WXrJG1Aov7E926bRUL8d+1OK0VPX19fjlL385eNasWafnzZt3pLa2Fg899NDAefPm9X/zzTdPuLKepkJDQ2u2bNnybcstPQMvWSPyEBs3bvTv1KlT/bx5884AgLe3N5YvX378gw8+6Hn+/Hmv2bNnBzdM4fTyyy/3AoB//OMfvsOHD4+Ijo6OHDNmTPixY8c6AMBrr73WMyYmJjIiIiLqrrvuCqusrPQCrNNYzZw5c0DTaaoOHjzYMTw8PLrhcUJCQkRUVFRkVFRUpP3Epp6CwUfkIfLy8nzi4uKumKQzMDCwvm/fvpf/9Kc/9Tx27FingoKCQovFUjhr1qwz1dXV8uSTT4Zs2LDhSEFBwYFHHnnk+6effro/AEybNq08Pz//wMGDBwsjIiIuLl26tPEsjJamqerXr1/tP/7xD0thYeGBDz744Nv58+e7/qTcNsahLpGHsM1WctWXhqqKXbt2+aemppZ16GCdVap37951X3/9dedDhw75jB8/3gxYh8pBQUE1ALB3716fRYsW9a+srDT98MMPpttvv72iYXstTVN1+fJlefTRRwcWFhb6eHl54dixY53a7E23EQYfkYcYOnToxQ0bNlwxQ/LZs2e9SktLO4aEhFQ3DUVVlcGDB1/85ptvippua/bs2YPWrVt3eNSoUReXLl3ao2HePKDlaapefvnl3r169ar58MMPv6uvr4ePj49Lj3feDBzqEnmISZMmVV66dMlr2bJlPQCgtrYWTzzxxIApU6Z8f+edd55fvnx5UE1NDQDg1KlTptjY2Etnz571/uyzz7oAQHV1tWRnZ3cGgAsXLniFhITUVFdXy+rVq52eZQUAKioqTH379q0xmUx44403etTV1bn4nbY9Bh+Rh/Dy8sLf/va3wx999FH3gQMHxgwaNCimU6dO9UuXLi2eP39+WXBw8OUhQ4ZER0RERP3lL38J7Ny5s65evfrIwoULgyMiIqKio6OjduzY4QcACxcuLBkxYkTkbbfdZg4PD790PXX8+te/Pv3+++/3iIuLG2KxWDrbzwTtKVqclqqttMW0VEadRqmtGPXz5LRU7ceNTEtFRNSuMPiIyHAYfERkOAw+IjIcBh8RGQ6Dj4gMh8FH5CH+4z/+o8/gwYOjzWZz1JAhQ6K2b9/eqskBMjMz/e0nFkhJSQltmIygJenp6QEikpCTk9O5Nft2xs6dO31nzpw5oOWWrcdL1ohaob7U7NLLtLz6WK45LdVnn33WZevWrQF5eXmFPj4+evLkSe/q6upWzcO3fft2fz8/v7qJEyf+cL2vXb16deCwYcOqMjIyAuPj411+N8WamhqMHTv2wtixYy+03Lr12OMj8gDFxcUdAgMDa318fBQA+vbtWxsaGloDABs2bPCPjIyMMpvNUVOmTAm9ePGiAED//v2Hnjx50huw9qJGjBgRcfDgwY7p6elBy5cv721/U/EdO3b4NZ2KqqmKigqv7Oxsv3feeefo+vXrG9tkZmb6Dx8+POKee+75SWhoaMwTTzzRPy0tLXDo0KGRZrM5qqCgoBMAlJSUeN91111hMTExkTExMZHbtm3rAgALFizoN3Xq1IGjR48OT05OHpSZmel/xx13DG7Y5/333x/aMN3WypUrAwBg2rRpITExMZGDBw+Onj9/fr/r/TwZfEQe4Je//OX5kpKSjqGhoTEPP/xwyKZNm/wA4MKFCzJnzpxBH3zwwRGLxVJYW1uLV199tdlbGEZERFyeMWNGWWpq6qmioqLCpKSkKqDlqagAYNWqVQHjxo2riI2NrQ4ICKjbtWtX4/1ci4qKfNLS0o4fOHCgYN26dT0sFkvnvLy8A9OnT//+tdde6wUAc+bMGbBgwYJT+fn5B9avX38kNTU1tOH1+/fv9926devhjRs3fme/z4ULF/bt2rVrncViKbRYLIX33ntvJQAsWbKkOD8//0BRUVFBVlaW/549e3yu5/NsV0Pd3tnV7i6BqE1069atPj8/v3DLli3+n3/+uf8jjzwStmjRohPDhw+/EBwcXB0bG1sNADNnzjzz+uuv9wJw+nq239JUVACwZs2awHnz5p0GgJSUlLMZGRmBY8aMuQAAQ4cO/WHgwIE1ABASElJ99913VwBAXFzcxYaZX7KysroeOnSoMaCqqqpM5eXlXgCQlJR0zs/P76rrZ3fu3Nl19erVjTM/BwUF1QHAu+++G7hy5cqetbW1UlZW1iE3N7fzyJEjLzr7fttV8BG1Z97e3rjvvvsq77vvvsrY2NiLGRkZPRITE5s9FmYymbS+3jp/wMWLF685umtpKqrS0lLT7t27u1osFp+5c+eirq5ORETT0tJOAECnTp0aX+Tl5dW4PS8vL9TV1UnDdrOzsw84CrguXbo4nOjANgfhFeuKioo6Llu2rPfevXsPBAUF1aWkpIReunTpukavHOoSeYDc3NxOeXl5jRN+5uTk+AQHB1/+6U9/eqm4uLhjfn5+JwBIT0/vcdttt1UCQHBw8OWsrCxfAFizZk3jMTl/f/+6yspK0/XsPyMjo3tycvKZkpKSvOLi4rzS0tL9wcHBl7dt2+bn7DbGjBlzfvHixb0alr/88ssWh6fjxo07v2TJksbXlJWVmcrLy00+Pj71gYGBdcePH/f+4osvul3PewEYfEQe4fz586YZM2YMCgsLizabzVFFRUU+ixcvLvH19dXly5cfnTJlSpjZbI7y8vLC008/XQYAixYtKnn22WdDEhISIkwmU2MvKyUl5dymTZsC7L/caMnatWt7JCcnl9uvmzx5cnlGRobTc/mtWLHi+L59+7qYzeaosLCw6GXLljV7LLLBK6+8cvLcuXOm8PDw6IiIiKjNmzf7jxo16mJMTMyF8PDw6OnTp4cmJCRUOVtDg3Y1LdX4ib936fbayvZPF7q7BKdwWqofcVoqz8RpqYiIbBh8RGQ4DD4iMhwGH5Fz6uvr61t1iRi5h+2/l8PTZBh8RM7JLysr68bw8wz19fVSVlbWDUC+o+d5AjM1i1fC/Ki2tnZWaWnp26WlpTFgh8ET1APIr62tneXoSQYfkRMSEhJOA5jk7jrINfiXi4gMh8FHRIbD4CMiw2HwEZHhMPiIyHAYfERkOAw+IjIcBh8RGQ6Dj4gMh8FHRIbD4CMiw2HwEZHhMPiIyHCcCj4RSRKRgyJyWEQc3ilHRP5VRApFpEBE3nNtmURErtPitFQiYgLwOoCJAE4A+FpEPlbVQrs24QCeAzBaVctFpJfjrRERuZ8zPb4RAA6r6reqehnAagCTm7R5DMDrqloOAKp62rVlEhG5jjPB1x/AcbvlE7Z19swAzCKSJSK7RSTJ0YZEZLaIZItIdllZWesqJiK6Qc4En6N7DDS9C7k3gHAA4wBMBfC2iARc9SLVFaqaqKqJQUEt3kSdiKhNOBN8JwAMsFsOBlDioM0GVa1R1e8AHIQ1CImIbjnOBN/XAMJFZJCIdATwIICPm7T5G4A7AEBEesI69P3WlYUSEblKi8GnqrUA5gLYCuAAgDWqWiAivxWRhpuvbAVwRkQKAfwdwDOqeqatiiYiuhFO3WVNVTcD2Nxk3SK7xwpgge2HiOiWxis3iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDhOBZ+IJInIQRE5LCILr9HufhFREUl0XYlERK7VYvCJiAnA6wDuBhAFYKqIRDlo5w/gSQB7XF0kEZErOdPjGwHgsKp+q6qXAawGMNlBu/8L4L8AXHJhfURELudM8PUHcNxu+YRtXSMRiQcwQFUzXVgbEVGbcCb4xME6bXxSxAvAHwE81eKGRGaLSLaIZJeVlTlfJRGRCzkTfCcADLBbDgZQYrfsDyAGwBcichTAzwB87OgLDlVdoaqJqpoYFBTU+qqJiG6AM8H3NYBwERkkIh0BPAjg44YnVbVCVXuqaqiqhgLYDWCSqma3ScVERDeoxeBT1VoAcwFsBXAAwBpVLRCR34rIpLYukIjI1bydaaSqmwFsbrJuUTNtx914WUREbYdXbhCR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDhOBZ+IJInIQRE5LCILHTy/QEQKRWS/iHwuIgNdXyoRkWu0GHwiYgLwOoC7AUQBmCoiUU2a5QBIVNVYAOsA/JerCyUichVnenwjABxW1W9V9TKA1QAm2zdQ1b+r6gXb4m4Awa4tk4jIdZwJvv4Ajtstn7Cta86jAD65kaKIiNqStxNtxME6ddhQ5GEAiQBub+b52QBmA0BISIiTJRIRuZYzPb4TAAbYLQcDKGnaSETuBPACgEmqWu1oQ6q6QlUTVTUxKCioNfUSEd0wZ4LvawDhIjJIRDoCeBDAx/YNRCQewJuwht5p15dJROQ6LQafqtYCmAtgK4ADANaoaoGI/FZEJtmavQrAD8BaEflGRD5uZnNERG7nzDE+qOpmAJubrFtk9/hOF9dFRNRmeOUGERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHAYfERkOg4+IDIfBR0SGw+AjIsNh8BGR4TD4iMhwGHxEZDgMPiIyHG93F0C3LtPne91dAlGbYI+PiAynXfX42EMhImc41eMTkSQROSgih0VkoYPnO4nIB7bn94hIqKsLJSJylRaDT0RMAF4HcDeAKABTRSSqSWxOsOEAAAjUSURBVLNHAZSr6mAAfwSw2NWFEhG5ijM9vhEADqvqt6p6GcBqAJObtJkM4F3b43UAJoiIuK5MIiLXcSb4+gM4brd8wrbOYRtVrQVQAaCHKwokInI1Z77ccNRz01a0gYjMBjDbtlglIged2L+79QTwvSs3aPDOsCd8ngNdvUG6tTgTfCcADLBbDgZQ0kybEyLiDaAbgLNNN6SqKwCsaF2p7iEi2aqa6O462gt+nnQrcGao+zWAcBEZJCIdATwI4OMmbT4G8Ijt8f0AtqvqVT0+IqJbQYs9PlWtFZG5ALYCMAH4b1UtEJHfAshW1Y8B/AVAhogchrWn92BbFk1EdCOEHbNrE5HZtiE6uQA/T7oVMPiIyHB4rS4RGQ6Dj4gMh8FHRIbD4CMiw2lX01LdKBHZCAdXnDRQ1Uk3sRyPJiKVuPZn2fUmlkN0BQbflf5g+50MoA+Av9qWpwI46o6CPJWq+gOA7XzPUgAZsF7aOA2AvxtLI+LpLI6IyE5VHdvSOmqZiOxR1ZEtrSO6mXiMz7EgEflJw4KIDAIQ5MZ6PFmdiEwTEZOIeInINAB17i6KjI1DXcfmA/hCRL61LYcCmOO+cjzaQwD+ZPtRAFm2dURuw6FuM0SkE4AhtsUiVa12Zz1E5Doc6jogIr4AngEwV1VzAYSIyH1uLssjiYhZRD4XkXzbcqyIvOjuusjYGHyOvQPgMoBRtuUTAP6f+8rxaG8BeA5ADQCo6n5w9h5yMwafY2Gq+l/48R/rRTieZZpa5quqXzVZV+uWSohsGHyOXRYRH9hOwBWRMAA8xtc639s+v4bP8n4AJ91bEhkdv9V17CUAWwAMEJFVAEYDmOnWijzX/4b1dgNDRKQYwHcAHnZvSWR0/Fa3GSLSA8DPYB3i7lZVl94gx2hEpAsAL1WtdHctRAw+OyIyRFWLRGSYo+dVdd/NrslTicjDqvpXEVng6HlVXXKzayJqwKHulRbAevvL1xw8pwDG39xyPJqv7Tevy6VbDoPPjqrOtv2+w921tANhtt+FqrrWrZUQNcGhbjNE5H/Beqla4x8HVU13W0EeRkTyAAwDsEdVHR46IHIX9vgcEJEMWHss3+DHC+oVAIPPeVsAfA+gi4ict1svAJTz8ZE7scfngIgcABDFm6LfOBHZoKqT3V0HkT2ewOxYPqwTkdINYujRrYhDXcd6AigUka9gd8UGp553nojsUtUxdlPQi/1vDnXJnTjUdUBEbne0XlV33OxaiMj1ONR1wBZwRwF0sD3+GgBPXm4F2xdFLa4jupkYfA6IyGMA1gF407aqP4C/ua8ijxZtvyAi3gAS3FQLEQAGX3P+N6wTE5wHAFU9BKCXWyvyMCLynO34XqyInLf9VAI4BWCDm8sjg2PwOVatqpcbFmy9FB4MvQ6q+gqAbgDSVbWr7cdfVXuo6nPuro+MjcHn2A4ReR6Aj4hMBLAWwEY31+RxVLUeQJy76yBqisHn2EIAZQDyYL272mYAvE9E6+wWkeHuLoLIHk9naYaIBAGAqpa5uxZPJiKFACJg/Zb8B/x4Hl+sO+siY2Pw2RERgXX25bmw/gMVWK/V/bOq/tadtXkqERnoaL2qHrvZtRA14FD3Sr+G9dvc4baD8IEARgIYLSLz3VuaZ7IF3AAA422PL4D/35GbscdnR0RyAExsOs28bdi7TVXj3VOZ5xKRlwAkAohQVbOI9AOwVlVHu7k0MjD+5b1SB0f31rAd5+vghnrag38BMAnW43tQ1RJwVmZyMwbflS638jlq3mXb9F4Nt5fs4uZ6iDg7SxNxTSbNbCAAOt/sYtqJNSLyJoAA26WA/w7gLTfXRAbHY3zUJkRkMIDeqpplOwn857D+ATkPYJWqHnFrgWRoDD5qEyKSCeB5Vd3fZH0igJdU9RfuqYyIx/io7YQ2DT0AUNVsWG/iROQ2DD5qK9c6Jupz06ogcoDBR23la9uXGVcQkUcB7HVDPUSNeIyP2oSI9AawHtbTgBqCLhFARwD/oqql7qqNiMFHbUpE7gAQY1ssUNXt7qyHCGDwEZEB8RgfERkOg6+dEpFfi4ivq9oRtScc6rZTInIUQKKjSRda046oPWGPr42IyAwR2S8iuSKSISK/EJE9IpIjIp/ZvvWEiNwuIt/YfnJExN+2/hkR+dq2jd9cYz9dRGSTbT/5IvKAiDwJoB+Av4vI323t0kQkW0QKGrbXTLsqu23fLyIrbY+n2LafKyI72+RDI7pZVJU/Lv6B9V6yBwH0tC0HAuiOH3vYswC8Znu8EcBo22M/WCeO+DmAFbBe2+oFIBPA2Gb2lQLgLbvlbrbfRxv231CD7bcJwBcAYptpV2X3+H4AK22P8wD0tz0OcPdnzB/+3MgPe3xtYzyAdWobPqrqWQDBALaKSB6AZ/DjjbazACyx9b4CVLUW1uD7OYAcAPsADAEQ3sy+8gDcKSKLReQ2Va1opt2/isg+2zajAURd53vKArDSdlKy6TpfS3RLYfC1DcHV9+H9M4BlqjoU1ju3dQYAVf09rD1AH1jvSDbE9vpXVPWntp/BqvoXRztSVQuABFgD8BURWXRVMSKDADwNYIJab/KzCc1fUmZfd2MbVU2F9U5zAwB8IyI9rvUBEN3KGHxt43NYe1g9AEBEAmG9uXax7flHGhqKSJiq5qnqYgDZsPbutgL4dxHxs7XpLyK9HO3INpX7BVX9K4A/ABhme6oSP8503BXWGZArbMcW77bbhH07ADglIpEi4gXr7Mn2de5R1UUAvoc1AIk8EicibQOqWiAiL8N6Y/I6WIeX/wlgrYgUA9gNYJCt+a9tVzfUASgE8ImqVotIJID/sd74DVUAHgZw2sHuhgJ4VUTqAdQAeNy2fgWAT0TkpKreYbufSAGAb2EdtsJRO1jvKZwJ4DiAfFiPO8K2j3BYe6OfA8ht/SdE5F48nYWIDIdDXSIyHA51PYTteOHnDp6aoKpnbnY9RJ6MQ10iMhwOdYnIcBh8RGQ4DD4iMhwGHxEZDoOPiAyHwUdEhvP/Ad5c4qAoQw/vAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "education_of_employee  Bachelor's  Doctorate  High School  Master's    All\n",
      "case_status                                                               \n",
      "All                         10220       2189         3416      9622  25447\n",
      "Certified                    6362       1910         1164      7565  17001\n",
      "Denied                       3858        279         2252      2057   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "has_job_experience      N      Y    All\n",
      "case_status                            \n",
      "All                 10661  14786  25447\n",
      "Certified            5986  11015  17001\n",
      "Denied               4675   3771   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "requires_job_training      N     Y    All\n",
      "case_status                              \n",
      "All                    22498  2949  25447\n",
      "Certified              15000  2001  17001\n",
      "Denied                  7498   948   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region_of_employment  Island  Midwest  Northeast  South  West    All\n",
      "case_status                                                         \n",
      "All                      375     4299       7189   7006  6578  25447\n",
      "Certified                226     3246       4524   4908  4097  17001\n",
      "Denied                   149     1053       2665   2098  2481   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit_of_wage  Hour  Month  Week   Year    All\n",
      "case_status                                  \n",
      "All           2153     89   272  22933  25447\n",
      "Certified      747     55   169  16030  17001\n",
      "Denied        1406     34   103   6903   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full_time_position     N      Y    All\n",
      "case_status                           \n",
      "All                 2706  22741  25447\n",
      "Certified           1855  15146  17001\n",
      "Denied               851   7595   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case_status  Certified  Denied    All\n",
      "case_status                          \n",
      "Denied               0    8446   8446\n",
      "All              17001    8446  25447\n",
      "Certified        17001       0  17001\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "company_size  0-1000  1000-2000  2000-3000  3000-4000  4000+    All\n",
      "case_status                                                        \n",
      "All             6208       5915       4978       3548   4798  25447\n",
      "Certified       4091       3878       3330       2341   3361  17001\n",
      "Denied          2117       2037       1648       1207   1437   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "company_age   50+  30-50  20-30  10-20  0-10    All\n",
      "case_status                                        \n",
      "All          5106   3739   3386   8367  4849  25447\n",
      "Certified    3385   2466   2226   5567  3357  17001\n",
      "Denied       1721   1273   1160   2800  1492   8446\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a table and stacked barplot for each variable with a data type of category by case_status\n",
    "cols_cat = df.select_dtypes(['category'])\n",
    "for i in cols_cat.columns:\n",
    "    stacked_barplot(df, \"case_status\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "68c3b566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a barplot of region of employment for an applicant's job with the corresponding percent of approved and denied cases for each\n",
    "labeled_barplot(df_annual,'region_of_employment','case_status',perc=True,order=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "583c9c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a barplot of size of the company that an applicant is applying for with the corresponding percent of approved and denied cases for each\n",
    "labeled_barplot(df_annual,'company_size','case_status',perc=True,order=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c74cc33d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a barplot of region of employment for an applicant's job with the corresponding percent of approved and denied cases for each\n",
    "labeled_barplot(df_annual,'company_age','case_status',perc=True,order=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "65500925",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent\n",
      "Africa          82817.44\n",
      "Asia            86237.72\n",
      "Europe          62148.78\n",
      "North America   80015.78\n",
      "Oceania         83806.39\n",
      "South America   73641.62\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "education_of_employee\n",
      "Bachelor's    81153.74\n",
      "Doctorate     67498.27\n",
      "High School   80905.55\n",
      "Master's      84849.96\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "has_job_experience\n",
      "N   86478.62\n",
      "Y   77881.05\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "requires_job_training\n",
      "N   80867.28\n",
      "Y   84473.58\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "region_of_employment\n",
      "Island      96323.02\n",
      "Midwest     94900.09\n",
      "Northeast   77091.74\n",
      "South       82174.41\n",
      "West        74720.82\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "unit_of_wage\n",
      "Hour         nan\n",
      "Month   87592.86\n",
      "Week    85606.82\n",
      "Year    81237.54\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "full_time_position\n",
      "N   105484.60\n",
      "Y    78161.02\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "case_status\n",
      "Certified   80816.02\n",
      "Denied      82459.91\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "company_size\n",
      "0-1000      83771.03\n",
      "1000-2000   82275.52\n",
      "2000-3000   83759.38\n",
      "3000-4000   80083.01\n",
      "4000+       75600.38\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "company_age\n",
      "50+     81273.99\n",
      "30-50   82004.74\n",
      "20-30   80962.70\n",
      "10-20   81613.29\n",
      "0-10    80552.43\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "income_per_year\n",
      "$0-25k        13113.11\n",
      "$25k-50k      38072.93\n",
      "$50k-75k      62637.38\n",
      "$75k-100k     87275.88\n",
      "$100k-125k   111915.85\n",
      "$125k+       161163.76\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n"
     ]
    }
   ],
   "source": [
    "#create tables listing the mean wage for each unique value within each variable with a data type category\n",
    "for col in df_annual.dtypes[df_annual.dtypes == 'category'].index:\n",
    "    print(df_annual.groupby(col)['prevailing_wage'].mean())\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d9f470e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent\n",
      "Africa          399.13\n",
      "Asia            421.97\n",
      "Europe          398.23\n",
      "North America   406.39\n",
      "Oceania         477.20\n",
      "South America   392.17\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "education_of_employee\n",
      "Bachelor's    413.02\n",
      "Doctorate     414.55\n",
      "High School   418.06\n",
      "Master's      415.42\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "has_job_experience\n",
      "N   414.31\n",
      "Y   415.57\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "requires_job_training\n",
      "N   415.88\n",
      "Y   382.34\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "region_of_employment\n",
      "Island      275.65\n",
      "Midwest     427.30\n",
      "Northeast   406.65\n",
      "South       426.43\n",
      "West        413.82\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "unit_of_wage\n",
      "Hour    414.77\n",
      "Month      nan\n",
      "Week       nan\n",
      "Year       nan\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "full_time_position\n",
      "N   488.47\n",
      "Y   414.12\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "case_status\n",
      "Certified   424.92\n",
      "Denied      409.38\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "company_size\n",
      "0-1000      391.91\n",
      "1000-2000   433.71\n",
      "2000-3000   415.45\n",
      "3000-4000   400.65\n",
      "4000+       441.46\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "company_age\n",
      "50+     415.77\n",
      "30-50   430.93\n",
      "20-30   396.79\n",
      "10-20   406.01\n",
      "0-10    429.35\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n",
      "income_per_day\n",
      "(0, 200]      111.90\n",
      "(200, 400]    297.49\n",
      "(400, 600]    500.06\n",
      "(600, 800]    698.38\n",
      "(800, 1000]   894.85\n",
      "Name: prevailing_wage, dtype: float64\n",
      " \n"
     ]
    }
   ],
   "source": [
    "#create tables listing the mean wage for each unique value within each variable with a data type category\n",
    "for col in df_hourly.dtypes[df_hourly.dtypes == 'category'].index:\n",
    "    print(df_hourly.groupby(col)['prevailing_wage'].mean())\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a420127",
   "metadata": {},
   "source": [
    "#### Additional EDA observations\n",
    "* In addition to the analysis above on the ratio of approved to denied applications by education, previous job experience, continent, prevailing wage, and unit of wage:\n",
    "    * If a job does or does not require training does not affect this ratio.\n",
    "    * If a job is a full-time position or not does not affect this ratio.\n",
    "    * If a job is located in the Midwest or South region, applicants have a higher ratio of approvals than those in the Northeast, West, and particularly the Island region.\n",
    "    * Jobs that applicants with doctorate degree are applying for jobs have the lowest mean prevailing wage of any education.\n",
    "    * Temporary (i.e., not full-time) jobs that applicants are applying for have a significantly higher prevailing wage than full-time positions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77df43bd",
   "metadata": {
    "id": "alleged-spirituality"
   },
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "- Preparing data for modeling \n",
    "- Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "53242a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the int_64 columns from the dataframe, since this information is captured through the features engineered above\n",
    "df_model = df.drop(labels=['no_of_employees','yr_of_estab'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39d9911",
   "metadata": {},
   "source": [
    "#### Encoding Certified as 1 and Denied as 0, as the OFLC wants to identify those visa applicants who are predicted to be approved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3d3dbb6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"case_status\"] = df[\"case_status\"].apply(lambda x: 1 if x == \"Certified\" else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bbb317f",
   "metadata": {
    "_cell_guid": "5e39d0b7-2f26-4d26-8a8d-6d0da0e22594",
    "_execution_state": "idle",
    "_uuid": "3c5ea745d170940b3d831b6e128959c87f8c117f"
   },
   "source": [
    "### Spliting the data \n",
    "We will use 70% of data for training and 30% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "76f19d9a",
   "metadata": {
    "_cell_guid": "5a9250a4-7c26-4770-943e-234568a37be3",
    "_execution_state": "idle",
    "_uuid": "00a1d497bd1fcae2c3500540bede6d2bbf25ab54"
   },
   "outputs": [],
   "source": [
    "#create a dataframe of the predictor feature columns\n",
    "X = df.drop('case_status',axis=1)\n",
    "#create a datafrane of the predicted class (1=True, 0=False)\n",
    "Y = df['case_status']\n",
    "\n",
    "#generate dummy variables for each categorical variable\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "#split the data into train and test datasets\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f09a11ab",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "34188146c68af804a7df3cf0e3e33ed79d096985"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.00% data is in training set\n",
      "30.00% data is in test set\n"
     ]
    }
   ],
   "source": [
    "#confirm the split\n",
    "print(\"{0:0.2f}% data is in training set\".format((len(x_train)/len(df.index)) * 100))\n",
    "print(\"{0:0.2f}% data is in test set\".format((len(x_test)/len(df.index)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6a2385ca",
   "metadata": {
    "_execution_state": "idle",
    "_uuid": "eca76e428776a0897acf958ad9791abd06700fe7",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Training set :  (17812, 27)\n",
      "Shape of test set :  (7635, 27)\n",
      " \n",
      "Percentage of classes in training set:\n",
      "1   0.67\n",
      "0   0.33\n",
      "Name: case_status, dtype: float64\n",
      " \n",
      "Percentage of classes in test set:\n",
      "1   0.67\n",
      "0   0.33\n",
      "Name: case_status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#confirm the shape of both data sets and the ratio of classes is the same across both train and test datasets\n",
    "print(\"Shape of Training set : \", x_train.shape)\n",
    "print(\"Shape of test set : \", x_test.shape)\n",
    "print(' ')\n",
    "print(\"Percentage of classes in training set:\")\n",
    "print(y_train.value_counts(normalize=True))\n",
    "print(' ')\n",
    "print(\"Percentage of classes in test set:\")\n",
    "print(y_test.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2939c3af",
   "metadata": {
    "id": "domestic-iceland"
   },
   "source": [
    "## Bagging and Boosting models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bfcdb3",
   "metadata": {},
   "source": [
    "### Model evaluation criterion\n",
    "\n",
    "#### Model can make wrong predictions as:\n",
    "1. Predicting an applicant should be approved the applicant is denied.\n",
    "2. Predicting an applicant should be denied and the applicant is approved.\n",
    "\n",
    "#### Which case is more important? \n",
    "* Both are important:\n",
    "    * If an applicant is approved when they would have been denied, an unqualified employee will get a job that should have been filled by a US citizen.\n",
    "    * If an applicant is denied when they should have been approved, U.S. companies will not be able to fill critical positions and the overall economy will not be as productive.\n",
    "\n",
    "#### How to reduce the losses?\n",
    "* As the process of reviewing each application is time and resource-intensive, this model should identify those candidates predicted to be approved, so agents can prioritize these applications.\n",
    "* F1 Score can be used a the metric for evaluation of the model, as the greater the F1 score, the higher the chances of minimizing False Negatives and False Positives.\n",
    "* We will use balanced class weights, where applicable, so that model focuses equally on both classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d60c13",
   "metadata": {},
   "source": [
    "#### Defining functions to provide metric scores (i.e., accuracy, recall, and precision) on the train and test datasets and to show the resulting confusion matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "faa6ddcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a function to compute different metrics to check performance of a classification model built using sklearn\n",
    "def model_performance_classification_sklearn(model, predictors, target):\n",
    "    \"\"\"\n",
    "    Function to compute different metrics to check classification model performance\n",
    "\n",
    "    model: classifier\n",
    "    predictors: independent variables\n",
    "    target: dependent variable\n",
    "    \"\"\"\n",
    "\n",
    "    # predicting using the independent variables\n",
    "    pred = model.predict(predictors)\n",
    "\n",
    "    acc = accuracy_score(target, pred)  # to compute Accuracy\n",
    "    recall = recall_score(target, pred)  # to compute Recall\n",
    "    precision = precision_score(target, pred)  # to compute Precision\n",
    "    f1 = f1_score(target, pred)  # to compute F1-score\n",
    "\n",
    "    # creating a dataframe of metrics\n",
    "    df_perf = pd.DataFrame(\n",
    "        {\n",
    "            \"Accuracy\": acc,\n",
    "            \"Recall\": recall,\n",
    "            \"Precision\": precision,\n",
    "            \"F1\": f1,\n",
    "        },\n",
    "        index=[0],\n",
    "    )\n",
    "\n",
    "    return df_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "69b4dc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix_sklearn(model, predictors, target):\n",
    "    \"\"\"\n",
    "    To plot the confusion_matrix with percentages\n",
    "\n",
    "    model: classifier\n",
    "    predictors: independent variables\n",
    "    target: dependent variable\n",
    "    \"\"\"\n",
    "    y_pred = model.predict(predictors)\n",
    "    cm = confusion_matrix(target, y_pred)\n",
    "    labels = np.asarray(\n",
    "        [\n",
    "            [\"{0:0.0f}\".format(item) + \"\\n{0:.2%}\".format(item / cm.flatten().sum())]\n",
    "            for item in cm.flatten()\n",
    "        ]\n",
    "    ).reshape(2, 2)\n",
    "\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=labels, fmt=\"\")\n",
    "    plt.ylabel(\"True label\")\n",
    "    plt.xlabel(\"Predicted label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7634e1cf",
   "metadata": {
    "id": "U_imjVzRxLQC"
   },
   "source": [
    "### Decision Tree Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da84ba2",
   "metadata": {
    "id": "fUTe3bPVxLQD"
   },
   "source": [
    "* We will build our model using the DecisionTreeClassifier function, using default 'gini' criteria to split. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "96b22c39",
   "metadata": {
    "id": "LYsH6SEbR2Zv"
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier(criterion='gini', random_state=1, class_weight='balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3a8b8650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=1, splitter='best')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9c732db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(dtree, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5d7842",
   "metadata": {},
   "source": [
    "**Confusion Matrix -**\n",
    "\n",
    "* Applicant was approved and the model predicted approval :  True Positive (observed=1,predicted=1)\n",
    "\n",
    "* Applicant was denied and the model predicted approval : False Positive (observed=0,predicted=1)\n",
    "\n",
    "* Applicant was denied and the model predicted denial : True Negative (observed=0,predicted=0)\n",
    "\n",
    "* Applicant was approved and the model predicted denial : False Negative (observed=1,predicted=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d484508c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      1.00    1.00       1.00 1.00\n"
     ]
    }
   ],
   "source": [
    "dtree_model_train_perf=model_performance_classification_sklearn(dtree, x_train, y_train)\n",
    "print(\"Training performance \\n\",dtree_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f92745e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.66    0.74       0.75 0.75\n"
     ]
    }
   ],
   "source": [
    "dtree_model_test_perf=model_performance_classification_sklearn(dtree, x_test, y_test)\n",
    "print(\"Testing performance \\n\",dtree_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca637519",
   "metadata": {},
   "source": [
    "* The Decision Tree is working well on the training data, but is not able to generalize well on the test data, indicating the model is overfit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9552dca6",
   "metadata": {},
   "source": [
    "### Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "da41428f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=None, bootstrap=True, bootstrap_features=False,\n",
       "                  max_features=1.0, max_samples=1.0, n_estimators=10,\n",
       "                  n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging = BaggingClassifier(random_state=1)\n",
    "bagging.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3dad2861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(bagging, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5b69f14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.98    0.98       0.99 0.99\n"
     ]
    }
   ],
   "source": [
    "bagging_model_train_perf=model_performance_classification_sklearn(bagging, x_train, y_train)\n",
    "print(\"Training performance \\n\",bagging_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e513e96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.69    0.77       0.77 0.77\n"
     ]
    }
   ],
   "source": [
    "bagging_model_test_perf=model_performance_classification_sklearn(bagging, x_test, y_test)\n",
    "print(\"Testing performance \\n\",bagging_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181ece74",
   "metadata": {},
   "source": [
    "* Similar to the Decision Tree model, this bagging classifer is overfit to the training dataset but has a higher F1 score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60da599a",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c8446437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(random_state=1)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "efed0b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(rf,x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cad96cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      1.00    1.00       1.00 1.00\n"
     ]
    }
   ],
   "source": [
    "rf_model_train_perf=model_performance_classification_sklearn(rf,x_train,y_train)\n",
    "print(\"Training performance \\n\",rf_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ac200342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.70    0.79       0.77 0.78\n"
     ]
    }
   ],
   "source": [
    "rf_model_test_perf=model_performance_classification_sklearn(rf,x_test,y_test)\n",
    "print(\"Testing performance \\n\",rf_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249802a7",
   "metadata": {},
   "source": [
    "* Similar to the models above, this Random Forest is also overfit to the training dataset but has the highest F1 score so far."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d38d0ec",
   "metadata": {
    "id": "GZw_di98pkfV"
   },
   "source": [
    "### AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4eae1e89",
   "metadata": {
    "id": "YElA_stqpkfV",
    "outputId": "f7c618fa-7d4a-4f9a-fab7-e8344079aaf2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
       "                   n_estimators=50, random_state=1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_classifier=AdaBoostClassifier(random_state=1)\n",
    "ab_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "07dd6a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(ab_classifier, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9cf92252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.88       0.76 0.82\n"
     ]
    }
   ],
   "source": [
    "ab_classifier_model_train_perf = model_performance_classification_sklearn(ab_classifier,x_train,y_train)\n",
    "print(\"Training performance \\n\",ab_classifier_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "44717ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.74    0.89       0.76 0.82\n"
     ]
    }
   ],
   "source": [
    "ab_classifier_model_test_perf = model_performance_classification_sklearn(ab_classifier,x_test,y_test)\n",
    "print(\"Testing performance \\n\",ab_classifier_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbb9efb",
   "metadata": {},
   "source": [
    "* This AdaBoost classifer performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* Additionally, the model performs the best of any model yet (i.e., highest F1 score) on the testing data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4faf623",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a1e25e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=1, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(random_state=1)\n",
    "gbc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f1bd3a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(gbc, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "92622f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.75    0.87       0.78 0.83\n"
     ]
    }
   ],
   "source": [
    "gbc_classifier_model_train_perf = model_performance_classification_sklearn(gbc,x_train,y_train)\n",
    "print(\"Training performance \\n\",gbc_classifier_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5a49482a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.75    0.88       0.78 0.83\n"
     ]
    }
   ],
   "source": [
    "gbc_classifier_model_test_perf = model_performance_classification_sklearn(gbc,x_test,y_test)\n",
    "print(\"Training performance \\n\",gbc_classifier_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2c8af4",
   "metadata": {},
   "source": [
    "* This Gradient Boosting classifer performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* Additionally, this model performs even better than the AdaBoost model above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a449ceea",
   "metadata": {},
   "source": [
    "### XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "62e4a17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              eval_metric='logloss', gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, objective='binary:logistic',\n",
       "              predictor='auto', random_state=1, reg_alpha=0, reg_lambda=1,\n",
       "              scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "              use_label_encoder=True, validate_parameters=1, ...)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(random_state=1,eval_metric='logloss')\n",
    "xgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1af6ce54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(xgb, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1fefd095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.81    0.92       0.82 0.87\n"
     ]
    }
   ],
   "source": [
    "xgb_classifier_model_train_perf = model_performance_classification_sklearn(xgb,x_train,y_train)\n",
    "print(\"Training performance \\n\",xgb_classifier_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e2ccae83",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.75    0.87       0.78 0.82\n"
     ]
    }
   ],
   "source": [
    "xgb_classifier_model_test_perf = model_performance_classification_sklearn(xgb,x_test,y_test)\n",
    "print(\"Training performance \\n\",xgb_classifier_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39515c4c",
   "metadata": {},
   "source": [
    "* This XGBoost classifer performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* Additionally, this model performs as well as the AdaBoost model and slightly worse the Gradient Boost model on the test data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff4f4f6",
   "metadata": {},
   "source": [
    "### Base models comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "aa3ebe8a",
   "metadata": {
    "id": "everyday-kinase",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance comparison of base models:\n"
     ]
    },
    {
     "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>Decision Tree</th>\n",
       "      <th>Bagging Classifier</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>AdaBoost Classifier</th>\n",
       "      <th>Gradient Boosting Classifier</th>\n",
       "      <th>XGBoost Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Decision Tree  Bagging Classifier  Random Forest  \\\n",
       "Accuracy            1.00                0.98           1.00   \n",
       "Recall              1.00                0.98           1.00   \n",
       "Precision           1.00                0.99           1.00   \n",
       "F1                  1.00                0.99           1.00   \n",
       "\n",
       "           AdaBoost Classifier  Gradient Boosting Classifier  \\\n",
       "Accuracy                  0.73                          0.75   \n",
       "Recall                    0.88                          0.87   \n",
       "Precision                 0.76                          0.78   \n",
       "F1                        0.82                          0.83   \n",
       "\n",
       "           XGBoost Classifier  \n",
       "Accuracy                 0.81  \n",
       "Recall                   0.92  \n",
       "Precision                0.82  \n",
       "F1                       0.87  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe comparing the performance of the base models on the training data\n",
    "\n",
    "models_train_comp_df = pd.concat(\n",
    "    [dtree_model_train_perf.T,\n",
    "     bagging_model_train_perf.T,\n",
    "     rf_model_train_perf.T,\n",
    "     ab_classifier_model_train_perf.T, \n",
    "     gbc_classifier_model_train_perf.T, \n",
    "     xgb_classifier_model_train_perf.T],\n",
    "    axis=1,\n",
    ")\n",
    "models_train_comp_df.columns = [\n",
    "    \"Decision Tree\",\n",
    "    \"Bagging Classifier\",\n",
    "    \"Random Forest\",\n",
    "    \"AdaBoost Classifier\",\n",
    "    \"Gradient Boosting Classifier\",\n",
    "    \"XGBoost Classifier\"]\n",
    "print(\"Training performance comparison of base models:\")\n",
    "models_train_comp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2670d244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance comparison of base models:\n"
     ]
    },
    {
     "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>Decision Tree</th>\n",
       "      <th>Bagging Classifier</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>AdaBoost Classifier</th>\n",
       "      <th>Gradient Boosting Classifier</th>\n",
       "      <th>XGBoost Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Decision Tree  Bagging Classifier  Random Forest  \\\n",
       "Accuracy            0.66                0.69           0.70   \n",
       "Recall              0.74                0.77           0.79   \n",
       "Precision           0.75                0.77           0.77   \n",
       "F1                  0.75                0.77           0.78   \n",
       "\n",
       "           AdaBoost Classifier  Gradient Boosting Classifier  \\\n",
       "Accuracy                  0.74                          0.75   \n",
       "Recall                    0.89                          0.88   \n",
       "Precision                 0.76                          0.78   \n",
       "F1                        0.82                          0.83   \n",
       "\n",
       "           XGBoost Classifier  \n",
       "Accuracy                 0.75  \n",
       "Recall                   0.87  \n",
       "Precision                0.78  \n",
       "F1                       0.82  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe comparing the performance of the base models on the testing data\n",
    "\n",
    "models_test_comp_df = pd.concat(\n",
    "    [dtree_model_test_perf.T,\n",
    "     bagging_model_test_perf.T,\n",
    "     rf_model_test_perf.T,\n",
    "     ab_classifier_model_test_perf.T, \n",
    "     gbc_classifier_model_test_perf.T, \n",
    "     xgb_classifier_model_test_perf.T],\n",
    "    axis=1,\n",
    ")\n",
    "models_test_comp_df.columns = [\n",
    "    \"Decision Tree\",\n",
    "    \"Bagging Classifier\",\n",
    "    \"Random Forest\",\n",
    "    \"AdaBoost Classifier\",\n",
    "    \"Gradient Boosting Classifier\",\n",
    "    \"XGBoost Classifier\"]\n",
    "print(\"Testing performance comparison of base models:\")\n",
    "models_test_comp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd0ea13",
   "metadata": {},
   "source": [
    "* Overall, the bagging models perform best on the training data, due to all three of the models overfitting this data set. \n",
    "* As shown above, all three of the boosting models do not overfit the data and achieve higher F1 scores on the training data set, with the Gradient Boosting classifier performing the best."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8a521c",
   "metadata": {
    "id": "prime-athletics"
   },
   "source": [
    "##  Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1afaaa",
   "metadata": {},
   "source": [
    "**Tuning Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "51c43842",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=5, max_features=None, max_leaf_nodes=4,\n",
       "                       min_impurity_decrease=0.0001, min_impurity_split=None,\n",
       "                       min_samples_leaf=3, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=1, splitter='best')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier. \n",
    "dtree_estimator = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {'max_depth': np.arange(5,30,5),\n",
    "              'min_samples_leaf': [3, 5, 7],\n",
    "              'max_leaf_nodes' : [2, 4, 6],\n",
    "              'min_impurity_decrease': [0.0001,0.001,0.01,0.1]\n",
    "             }\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(dtree_estimator, parameters, scoring=scorer, cv=5)\n",
    "grid_obj = grid_obj.fit(x_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "dtree_estimator = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "dtree_estimator.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "985b574f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(dtree_estimator, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7cbc0249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.91       0.74 0.82\n"
     ]
    }
   ],
   "source": [
    "dtree_estimator_model_train_perf=model_performance_classification_sklearn(dtree_estimator, x_train,y_train)\n",
    "print(\"Training performance \\n\",dtree_estimator_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "83fc1249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.91       0.74 0.82\n"
     ]
    }
   ],
   "source": [
    "dtree_estimator_model_test_perf=model_performance_classification_sklearn(dtree_estimator, x_test, y_test)\n",
    "print(\"Testing performance \\n\",dtree_estimator_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbe7371",
   "metadata": {},
   "source": [
    "* The Decision Tree model after tuning performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* Additionally, this model performs as well as two out of the three boosting models above on the test data.\n",
    "* Both of these indicate this tuned model is a significant improvements over the base model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12e355d",
   "metadata": {},
   "source": [
    "**Tuning Bagging Classifier**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ddabeb9",
   "metadata": {},
   "source": [
    "Hyperparameters available for bagging classifier include:\n",
    "   - base_estimator: The base estimator to fit on random subsets of the dataset. If None(default), then the base estimator is a decision tree.\n",
    "   - n_estimators: The number of trees in the forest, default = 100.\n",
    "   - max_features: The number of features to consider when looking for the best split. \n",
    "   - bootstrap: Whether bootstrap samples are used when building trees. If False, the entire dataset is used to build each tree, default=True.\n",
    "   - bootstrap_features: If it is true, then features are drawn with replacement. Default value is False.\n",
    "   - max_samples: If bootstrap is True, then the number of samples to draw from X to train each base estimator. If None (default), then draw N samples, where N is the number of observations in the train data.\n",
    "   - oob_score: Whether to use out-of-bag samples to estimate the generalization accuracy, default=False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f80d317d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=None, bootstrap=True, bootstrap_features=False,\n",
       "                  max_features=0.8, max_samples=0.8, n_estimators=70,\n",
       "                  n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
       "                  warm_start=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random search for bagging classifier\n",
    "parameters = {'max_samples': [0.7,0.8,0.9,1],\n",
    "              'max_features': [0.7,0.8,0.9,1],\n",
    "              'n_estimators' : np.arange(50, 100, 10)\n",
    "             }\n",
    "\n",
    "#run the randomized search\n",
    "rand_bag = RandomizedSearchCV(BaggingClassifier(random_state=1,bootstrap=True), \n",
    "                              parameters, scoring = 'f1', cv = 5, random_state=1)\n",
    "rand_bag = rand_bag.fit(x_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "bag_tuned = rand_bag.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data\n",
    "bag_tuned.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4f746c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(bag_tuned, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d857e3ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.99    1.00       0.99 0.99\n"
     ]
    }
   ],
   "source": [
    "bagging_estimator_model_train_perf=model_performance_classification_sklearn(bag_tuned, x_train,y_train)\n",
    "print(\"Training performance \\n\",bagging_estimator_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "92f80d68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.87       0.76 0.81\n"
     ]
    }
   ],
   "source": [
    "bagging_estimator_model_test_perf=model_performance_classification_sklearn(bag_tuned, x_test, y_test)\n",
    "print(\"Testing performance \\n\",bagging_estimator_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977b20e7",
   "metadata": {},
   "source": [
    "* The Bagging Classifier after tuning performs far less well on the test data than on the training data, indicating that the model is still overfit. \n",
    "* However, this model does perform better than the base model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58774b9a",
   "metadata": {},
   "source": [
    "**Tuning Random Forest**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b3931c",
   "metadata": {},
   "source": [
    "Hyperparameters for Random Forest include:\n",
    "\n",
    "- n_estimators: The number of trees in the forest, default = 100.\n",
    "- max_features: The number of features to consider when looking for the best split. \n",
    "- class_weight: Weights associated with classes in the form {class_label: weight}.If not given, all classes are supposed to have weight one.  \n",
    "- For example: If the frequency of class 0 is 80% and the frequency of class 1 is 20% in the data, then class 0 will become the dominant class and the model will become biased toward the dominant classes. In this case, we can pass a dictionary {0:0.2,1:0.8} to the model to specify the weight of each class and the random forest will give more weightage to class 1. \n",
    "- bootstrap: Whether bootstrap samples are used when building trees. If False, the entire dataset is used to build each tree, default=True.\n",
    "- max_samples: If bootstrap is True, then the number of samples to draw from X to train each base estimator. If None (default), then draw N samples, where N is the number of observations in the train data.\n",
    "- oob_score: Whether to use out-of-bag samples to estimate the generalization accuracy, default=False.\n",
    "\n",
    "- Note: A lot of hyperparameters of Decision Trees are also available to tune  Random Forest like max_depth, min_sample_split etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b806cabf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=5, max_features=None, max_leaf_nodes=4,\n",
       "                       min_impurity_decrease=0.0001, min_impurity_split=None,\n",
       "                       min_samples_leaf=3, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=1, splitter='best')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier. \n",
    "rf_estimator = RandomForestClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {\"n_estimators\": np.arange(10, 40, 10),\n",
    "              \"min_samples_leaf\": np.arange(5, 10),\n",
    "              \"min_samples_split\": [3, 5, 7],\n",
    "              \"max_features\": [\"sqrt\", \"log2\"],\n",
    "              \"max_samples\": np.arange(0.3, 0.7, 0.1),\n",
    "}\n",
    "\n",
    "# Run the randomized search\n",
    "rand_rf = RandomizedSearchCV(rf_estimator, parameters, scoring='f1', cv=5, n_jobs=-1, random_state=1)\n",
    "rand_rf = grid_obj.fit(x_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "rf_estimator = rand_rf.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "rf_estimator.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "629c84a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(rf_estimator, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e3dc63e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.91       0.74 0.82\n"
     ]
    }
   ],
   "source": [
    "rf_estimator_model_train_perf=model_performance_classification_sklearn(rf_estimator, x_train,y_train)\n",
    "print(\"Training performance \\n\",rf_estimator_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e29da4a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.91       0.74 0.82\n"
     ]
    }
   ],
   "source": [
    "rf_estimator_model_test_perf=model_performance_classification_sklearn(rf_estimator, x_test, y_test)\n",
    "print(\"Testing performance \\n\",rf_estimator_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad19b6fc",
   "metadata": {},
   "source": [
    "* The Random Forest after tuning performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* Additionally, this model performs as well as two out of the three boosting models and the tuned Decision Tree on the test data.\n",
    "* Both of these indicate this tuned model is a significant improvements over the base model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19823fd9",
   "metadata": {},
   "source": [
    "#### Tuning AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "015c05a4",
   "metadata": {},
   "source": [
    "Hyperparameters include:\n",
    "   - base_estimator: The base estimator from which the boosted ensemble is built. By default the base estimator is a decision tree with max_depth=1\n",
    "   - n_estimators: The maximum number of estimators at which boosting is terminated. Default value is 50.\n",
    "   - learning_rate: Learning rate shrinks the contribution of each classifier by learning_rate. There is a trade-off between learning_rate and n_estimators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "273d803a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "                   base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                         class_weight='balanced',\n",
       "                                                         criterion='gini',\n",
       "                                                         max_depth=1,\n",
       "                                                         max_features=None,\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_impurity_split=None,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         presort='deprecated',\n",
       "                                                         random_state=1,\n",
       "                                                         splitter='best'),\n",
       "                   learning_rate=0.5, n_estimators=20, random_state=1)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier. \n",
    "abc_tuned = AdaBoostClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "## add from article\n",
    "parameters = {\n",
    "    #Let's try different max_depth for base_estimator\n",
    "    \"base_estimator\":[DecisionTreeClassifier(max_depth=1, random_state=1, class_weight='balanced'),\n",
    "                      DecisionTreeClassifier(max_depth=2, random_state=1, class_weight='balanced'),\n",
    "                      DecisionTreeClassifier(max_depth=3, random_state=1, class_weight='balanced')],\n",
    "    \"n_estimators\": np.arange(10,110,10),\n",
    "    \"learning_rate\":np.arange(0.1,2,0.1)\n",
    "}\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "acc_scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the randomized search\n",
    "rand_abc = RandomizedSearchCV(abc_tuned, parameters, scoring=acc_scorer,cv=5, n_jobs=-1, random_state=1)\n",
    "rand_abc = rand_abc.fit(x_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "abc_tuned = rand_abc.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data.\n",
    "abc_tuned.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "54393972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(abc_tuned, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7f7ba55a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.72    0.78       0.79 0.79\n"
     ]
    }
   ],
   "source": [
    "abc_tuned_model_train_perf=model_performance_classification_sklearn(abc_tuned, x_train,y_train)\n",
    "print(\"Training performance \\n\",abc_tuned_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "272d7b98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.72    0.79       0.80 0.79\n"
     ]
    }
   ],
   "source": [
    "abc_tuned_model_test_perf=model_performance_classification_sklearn(abc_tuned, x_test, y_test)\n",
    "print(\"Testing performance \\n\",abc_tuned_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d506d74",
   "metadata": {},
   "source": [
    "* The AdaBoost classifier after tuning performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* However, this model performs worse than the base model on both the train and test data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3302608",
   "metadata": {
    "id": "AbxL7YlzpkfY"
   },
   "source": [
    "#### Tuning GradientBoost Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa45c7c",
   "metadata": {},
   "source": [
    "Most of the hyperparameters available match those of a random forest classifier.\n",
    "  - init: An estimator object that is used to compute the initial predictions. If ‘zero’, the initial raw predictions are set to zero. By default, a DummyEstimator predicting the classes priors is used.\n",
    "  - There is no class_weights parameter in gradient boosting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fb04eda6",
   "metadata": {
    "id": "4ENuCKyhpkfY",
    "outputId": "c3acdbd7-c36a-40b9-cd67-adc14b9d4d9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=0.7, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=300,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=1, subsample=1, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier. \n",
    "gb_tuned = GradientBoostingClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {\"n_estimators\": [150,200,250,300],\n",
    "              \"subsample\":[0.8,0.9,1],\n",
    "              \"max_features\":[0.7,0.8,0.9,1],\n",
    "              \"learning_rate\": np.arange(0.1, 0.4, 0.1)}\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the randomized search\n",
    "rand_gb = RandomizedSearchCV(gb_tuned, parameters, scoring=scorer,cv=5, n_jobs=-1, random_state=1)\n",
    "rand_gb = rand_gb.fit(x_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "gb_tuned = rand_gb.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "gb_tuned.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "739dff8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a confusion matrix of the classifier's performance on the testing data\n",
    "confusion_matrix_sklearn(gb_tuned, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1a0350c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.91       0.74 0.82\n"
     ]
    }
   ],
   "source": [
    "gb_tuned_model_train_perf=model_performance_classification_sklearn(rf_estimator, x_train,y_train)\n",
    "print(\"Training performance \\n\",gb_tuned_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c48b3acc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.91       0.74 0.82\n"
     ]
    }
   ],
   "source": [
    "gb_tuned_model_test_perf=model_performance_classification_sklearn(rf_estimator, x_test, y_test)\n",
    "print(\"Testing performance \\n\",gb_tuned_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a63e32",
   "metadata": {},
   "source": [
    "* The Gradient Boosting classifier after tuning performs equally well on the training and testing datasets, indicating that the model is not overfit.\n",
    "* However, this model performs slightly worse than the base model on both the train and test data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a576695b",
   "metadata": {
    "id": "cnXZB3wLpkfa"
   },
   "source": [
    "#### Tuning XGB Classifer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6912ef5",
   "metadata": {},
   "source": [
    "Important parameters include:\n",
    "- scale_pos_weight:Control the balance of positive and negative weights, useful for unbalanced classes. It has range from 0 to $\\infty$.\n",
    "- subsample: Corresponds to the fraction of observations (the rows) to subsample at each step. By default it is set to 1 meaning that we use all rows.\n",
    "- colsample_bytree: Corresponds to the fraction of features (the columns) to use.\n",
    "- colsample_bylevel: The subsample ratio of columns for each level. Columns are subsampled from the set of columns chosen for the current tree.\n",
    "- colsample_bynode: The subsample ratio of columns for each node (split). Columns are subsampled from the set of columns chosen for the current level.\n",
    "- max_depth: is the maximum number of nodes allowed from the root to the farthest leaf of a tree.\n",
    "- learning_rate/eta: Makes the model more robust by shrinking the weights on each step.\n",
    "- gamma: A node is split only when the resulting split gives a positive reduction in the loss function. Gamma specifies the minimum loss reduction required to make a split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3a6e54e7",
   "metadata": {
    "id": "g0OwMXYNpkfb",
    "outputId": "68c07830-efa0-4b65-b829-49bd5b008635",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.5,\n",
       "              colsample_bynode=1, colsample_bytree=0.5,\n",
       "              enable_categorical=False, eval_metric='logloss', gamma=0,\n",
       "              gpu_id=-1, importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.2, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=90, n_jobs=8, num_parallel_tree=1,\n",
       "              objective='binary:logistic', predictor='auto', random_state=1,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=2, subsample=1,\n",
       "              tree_method='exact', use_label_encoder=True,\n",
       "              validate_parameters=1, ...)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier. \n",
    "xgb_tuned = XGBClassifier(random_state=1, eval_metric='logloss')\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {\"n_estimators\": np.arange(10,100,20),\n",
    "              \"scale_pos_weight\":[0,1,2,5],\n",
    "              \"subsample\":[0.5,0.7,0.9,1],\n",
    "              \"learning_rate\":[0.01,0.1,0.2,0.05],\n",
    "              \"gamma\":[0,1,3],\n",
    "              \"colsample_bytree\":[0.5,0.7,0.9,1],\n",
    "              \"colsample_bylevel\":[0.5,0.7,0.9,1]}\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the randomized search\n",
    "rand_xgb = RandomizedSearchCV(xgb_tuned, parameters, scoring=scorer,cv=5, n_jobs=-1, random_state=1)\n",
    "rand_xgb = rand_xgb.fit(x_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "xgb_tuned = rand_xgb.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "xgb_tuned.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "991f5886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(xgb_tuned, x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cd14ba30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.75    0.97       0.74 0.84\n"
     ]
    }
   ],
   "source": [
    "xgb_tuned_model_train_perf=model_performance_classification_sklearn(xgb_tuned, x_train,y_train)\n",
    "print(\"Training performance \\n\",xgb_tuned_model_train_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "fb0fc94f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance \n",
      "    Accuracy  Recall  Precision   F1\n",
      "0      0.73    0.95       0.73 0.82\n"
     ]
    }
   ],
   "source": [
    "xgb_tuned_model_test_perf=model_performance_classification_sklearn(xgb_tuned, x_test, y_test)\n",
    "print(\"Testing performance \\n\",xgb_tuned_model_test_perf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee539e7",
   "metadata": {},
   "source": [
    "* The XGBoost classifier after tuning is less overfit than the base model, but performs equally well on the test data.\n",
    "* This indicates a minor improvement over the base model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39fd592",
   "metadata": {},
   "source": [
    "### Tuned models comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f7b11bcc",
   "metadata": {
    "id": "everyday-kinase",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance comparison of tuned models:\n"
     ]
    },
    {
     "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>Tuned Decision Tree</th>\n",
       "      <th>Tuned Bagging Classifier</th>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <th>Tuned AdaBoost Classifier</th>\n",
       "      <th>Tuned Gradient Boosting Classifier</th>\n",
       "      <th>Tuned XGBoost Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Tuned Decision Tree  Tuned Bagging Classifier  Tuned Random Forest  \\\n",
       "Accuracy                  0.73                      0.99                 0.73   \n",
       "Recall                    0.91                      1.00                 0.91   \n",
       "Precision                 0.74                      0.99                 0.74   \n",
       "F1                        0.82                      0.99                 0.82   \n",
       "\n",
       "           Tuned AdaBoost Classifier  Tuned Gradient Boosting Classifier  \\\n",
       "Accuracy                        0.72                                0.73   \n",
       "Recall                          0.78                                0.91   \n",
       "Precision                       0.79                                0.74   \n",
       "F1                              0.79                                0.82   \n",
       "\n",
       "           Tuned XGBoost Classifier  \n",
       "Accuracy                       0.75  \n",
       "Recall                         0.97  \n",
       "Precision                      0.74  \n",
       "F1                             0.84  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe comparing the performance of the tuned models on the training data\n",
    "\n",
    "tuned_models_train_comp_df = pd.concat(\n",
    "    [dtree_estimator_model_train_perf.T,\n",
    "     bagging_estimator_model_train_perf.T,\n",
    "     rf_estimator_model_train_perf.T,\n",
    "     abc_tuned_model_train_perf.T, \n",
    "     gb_tuned_model_train_perf.T, \n",
    "     xgb_tuned_model_train_perf.T],\n",
    "    axis=1,\n",
    ")\n",
    "tuned_models_train_comp_df.columns = [\n",
    "    \"Tuned Decision Tree\",\n",
    "    \"Tuned Bagging Classifier\",\n",
    "    \"Tuned Random Forest\",\n",
    "    \"Tuned AdaBoost Classifier\",\n",
    "    \"Tuned Gradient Boosting Classifier\",\n",
    "    \"Tuned XGBoost Classifier\"]\n",
    "print(\"Training performance comparison of tuned models:\")\n",
    "tuned_models_train_comp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2f633db2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance comparison of tuned models:\n"
     ]
    },
    {
     "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>Tuned Decision Tree</th>\n",
       "      <th>Tuned Bagging Classifier</th>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <th>Tuned AdaBoost Classifier</th>\n",
       "      <th>Tuned Gradient Boosting Classifier</th>\n",
       "      <th>Tuned XGBoost Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Tuned Decision Tree  Tuned Bagging Classifier  Tuned Random Forest  \\\n",
       "Accuracy                  0.73                      0.73                 0.73   \n",
       "Recall                    0.91                      0.87                 0.91   \n",
       "Precision                 0.74                      0.76                 0.74   \n",
       "F1                        0.82                      0.81                 0.82   \n",
       "\n",
       "           Tuned AdaBoost Classifier  Tuned Gradient Boosting Classifier  \\\n",
       "Accuracy                        0.72                                0.73   \n",
       "Recall                          0.79                                0.91   \n",
       "Precision                       0.80                                0.74   \n",
       "F1                              0.79                                0.82   \n",
       "\n",
       "           Tuned XGBoost Classifier  \n",
       "Accuracy                       0.73  \n",
       "Recall                         0.95  \n",
       "Precision                      0.73  \n",
       "F1                             0.82  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe comparing the performance of the tuned models on the testing data\n",
    "\n",
    "tuned_models_test_comp_df = pd.concat(\n",
    "    [dtree_estimator_model_test_perf.T,\n",
    "     bagging_estimator_model_test_perf.T,\n",
    "     rf_estimator_model_test_perf.T,\n",
    "     abc_tuned_model_test_perf.T, \n",
    "     gb_tuned_model_test_perf.T, \n",
    "     xgb_tuned_model_test_perf.T],\n",
    "    axis=1,\n",
    ")\n",
    "tuned_models_test_comp_df.columns = [\n",
    "    \"Tuned Decision Tree\",\n",
    "    \"Tuned Bagging Classifier\",\n",
    "    \"Tuned Random Forest\",\n",
    "    \"Tuned AdaBoost Classifier\",\n",
    "    \"Tuned Gradient Boosting Classifier\",\n",
    "    \"Tuned XGBoost Classifier\"]\n",
    "print(\"Testing performance comparison of tuned models:\")\n",
    "tuned_models_test_comp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b632bb",
   "metadata": {
    "id": "obvious-maine"
   },
   "source": [
    "## Model Performance Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5aa8a807",
   "metadata": {
    "scrolled": true
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Bagging Classifier</th>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging Classifier</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost Classifier</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned XGBoost Classifier</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting Classifier</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Decision Tree</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Gradient Boosting Classifier</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost Classifier</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned AdaBoost Classifier</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Accuracy  Recall  Precision   F1\n",
       "Decision Tree                           1.00    1.00       1.00 1.00\n",
       "Random Forest                           1.00    1.00       1.00 1.00\n",
       "Tuned Bagging Classifier                0.99    1.00       0.99 0.99\n",
       "Bagging Classifier                      0.98    0.98       0.99 0.99\n",
       "XGBoost Classifier                      0.81    0.92       0.82 0.87\n",
       "Tuned XGBoost Classifier                0.75    0.97       0.74 0.84\n",
       "Gradient Boosting Classifier            0.75    0.87       0.78 0.83\n",
       "Tuned Decision Tree                     0.73    0.91       0.74 0.82\n",
       "Tuned Random Forest                     0.73    0.91       0.74 0.82\n",
       "Tuned Gradient Boosting Classifier      0.73    0.91       0.74 0.82\n",
       "AdaBoost Classifier                     0.73    0.88       0.76 0.82\n",
       "Tuned AdaBoost Classifier               0.72    0.78       0.79 0.79"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe comparing the performance all models on the training data\n",
    "all_models_train_comp = pd.concat([models_train_comp_df,tuned_models_train_comp_df],axis=1)\n",
    "all_models_train_comp.T.sort_values('F1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d24ef73d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The best performing model on the training data is the Decision Tree'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'The best performing model on the training data is the ' + all_models_train_comp.T.sort_values('F1',ascending=False).index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c990bcbf",
   "metadata": {
    "scrolled": true
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting Classifier</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned XGBoost Classifier</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost Classifier</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost Classifier</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Decision Tree</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Gradient Boosting Classifier</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Bagging Classifier</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned AdaBoost Classifier</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging Classifier</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Accuracy  Recall  Precision   F1\n",
       "Gradient Boosting Classifier            0.75    0.88       0.78 0.83\n",
       "Tuned XGBoost Classifier                0.73    0.95       0.73 0.82\n",
       "AdaBoost Classifier                     0.74    0.89       0.76 0.82\n",
       "XGBoost Classifier                      0.75    0.87       0.78 0.82\n",
       "Tuned Decision Tree                     0.73    0.91       0.74 0.82\n",
       "Tuned Random Forest                     0.73    0.91       0.74 0.82\n",
       "Tuned Gradient Boosting Classifier      0.73    0.91       0.74 0.82\n",
       "Tuned Bagging Classifier                0.73    0.87       0.76 0.81\n",
       "Tuned AdaBoost Classifier               0.72    0.79       0.80 0.79\n",
       "Random Forest                           0.70    0.79       0.77 0.78\n",
       "Bagging Classifier                      0.69    0.77       0.77 0.77\n",
       "Decision Tree                           0.66    0.74       0.75 0.75"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe comparing the performance of all models on the testing data\n",
    "all_models_test_comp = pd.concat([models_test_comp_df,tuned_models_test_comp_df],axis=1)\n",
    "all_models_test_comp.T.sort_values('F1',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c29cd832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The best performing model on the testing data is the Gradient Boosting Classifier'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'The best performing model on the testing data is the ' + all_models_test_comp.T.sort_values('F1',ascending=False).index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a852d27d",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "* As expected the Decision Tree model performs the best on the training dataset, since the model will significantly overfit this data unless pruned otherwise.\n",
    "* The base Gradient Boosting classifier performs the best on the testing dataset, with an 0.83 F1 score.\n",
    "* Interestingly, **six** models tied for the second-best performance, with an F1 score of 0.82."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1a4f4e",
   "metadata": {},
   "source": [
    "### Feature importance of: Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "aeb0d7ae",
   "metadata": {
    "id": "U7sE1kpqxLQW",
    "outputId": "71aa0d79-d32c-4288-ff74-f930c831622d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Imp\n",
      "education_of_employee_High School 0.29\n",
      "has_job_experience_Y              0.17\n",
      "prevailing_wage                   0.11\n",
      "education_of_employee_Master's    0.09\n",
      "education_of_employee_Doctorate   0.09\n",
      "unit_of_wage_Year                 0.06\n",
      "continent_Europe                  0.06\n",
      "region_of_employment_Midwest      0.03\n",
      "region_of_employment_South        0.02\n",
      "continent_North America           0.02\n"
     ]
    }
   ],
   "source": [
    "\"\"\"The importance of features in the tree building \n",
    "(The importance of a feature is computed as the (normalized) total \n",
    "reduction of the criterion brought by that feature.)\"\"\"\n",
    "\n",
    "print(pd.DataFrame(gbc.feature_importances_, columns = [\"Imp\"], \n",
    "                   index = x_train.columns).sort_values(by = 'Imp', ascending = False).head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "bf002ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = x_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "cf7d92c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = gbc.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], align='center')\n",
    "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a424a3ba",
   "metadata": {},
   "source": [
    "### Visualization of Decision Tree (tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "26b07cfa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create visualization of tuned decision-tree model\n",
    "feature_names = list(X.columns)\n",
    "plt.figure(figsize=(16,8))\n",
    "tree.plot_tree(dtree_estimator,feature_names=feature_names,filled=True,fontsize=12,node_ids=True,class_names=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b68f44c",
   "metadata": {
    "id": "nasty-retailer"
   },
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0169f1c8",
   "metadata": {
    "id": "amino-prediction"
   },
   "source": [
    "#### Insights\n",
    "For the Office of Foreign Labor Certification (OFLC), the three most critical components for pre-screening an applicant are:\n",
    "* *Education level*\n",
    "    * An applicant applying for a job requiring a high school diploma will more than likely be denied. Conversely, applications for jobs requiring a Master's degree or doctorate are very likely to be approved.\n",
    "* *Prior job experience*\n",
    "    * An applicant applying for a job without any previous job experience is more likley to be denied than an applicant for a job with experience.\n",
    "* *Prevailing wage*\n",
    "    * The higher the prevailing wage of the job an applicant is applying for, the more likely the application will be approved. This is especially true for applications for jobs with an hourly unit of wage.\n",
    "    \n",
    "#### Recommendations\n",
    "* To prioritize limited resources towards screening a batch of applications for those most likely to be approved, the OFLC can:\n",
    "    * Sort applications by level of education and review the higher levels of education first.\n",
    "    * Sort applications by previous job experience and review those with experience first.\n",
    "    * Divide applications for jobs into those with an hourly wage and those with an annual wage, sort each group by the prevailing wage, then review applications for salaried jobs first from highest to lowest wage.\n",
    "* As stated previously, the Gradient Boosting classifier performs the best of all the models created. However, as shown above, the tuned Decision-Tree model performs barely worse by F1 score and is a far simpler model. This model may be preferable if post-hoc explanations of OFLC decision-making is expected to be required.\n",
    "    * Furthermore, OFLC should examine more thoroughly why whether an application will be certified or denied can be very well predicted through just three nodes as shown above. \n",
    "    * For those in less skilled, entry-level, and/or hourly jobs, the system would appear to be biased against these applications being certified."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "EasyVisa_Project_Template_Notebook.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}