diff --git "a/AdultNoteBook/Kernels/ExtraTrees/5-adult-census-income-eda-and-prediction-87-35.ipynb" "b/AdultNoteBook/Kernels/ExtraTrees/5-adult-census-income-eda-and-prediction-87-35.ipynb"
new file mode 100644--- /dev/null
+++ "b/AdultNoteBook/Kernels/ExtraTrees/5-adult-census-income-eda-and-prediction-87-35.ipynb"
@@ -0,0 +1,2401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adult Census Income EDA and Prediction\n",
+    "\n",
+    "In this kernel I work with the UCI Adult Census Income dataset. The prediction task is to determine whether a person makes over $50K a year. I start with an exhaustive EDA, and I then train various models to solve the prediction task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "#print(os.listdir(\"../input\"))\n",
+    "\n",
+    "data = pd.read_csv(\"../../Data/adult.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from aif360.datasets import StandardDataset\n",
+    "from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric\n",
+    "import matplotlib.patches as patches\n",
+    "from aif360.algorithms.preprocessing import Reweighing\n",
+    "#from packages import *\n",
+    "#from ml_fairness import *\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "\n",
+    "from IPython.display import Markdown, display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32561\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>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education.num</th>\n",
+       "      <th>marital.status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital.gain</th>\n",
+       "      <th>capital.loss</th>\n",
+       "      <th>hours.per.week</th>\n",
+       "      <th>native.country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>?</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>?</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>7th-8th</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>264663</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Own-child</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>34</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>216864</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Other-service</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3770</td>\n",
+       "      <td>45</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>150601</td>\n",
+       "      <td>10th</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3770</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>74</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>88638</td>\n",
+       "      <td>Doctorate</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Other-relative</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3683</td>\n",
+       "      <td>20</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>68</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>422013</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3683</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>41</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>70037</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Craft-repair</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3004</td>\n",
+       "      <td>60</td>\n",
+       "      <td>?</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age    workclass  fnlwgt     education  education.num marital.status  \\\n",
+       "0   90            ?   77053       HS-grad              9        Widowed   \n",
+       "1   82      Private  132870       HS-grad              9        Widowed   \n",
+       "2   66            ?  186061  Some-college             10        Widowed   \n",
+       "3   54      Private  140359       7th-8th              4       Divorced   \n",
+       "4   41      Private  264663  Some-college             10      Separated   \n",
+       "5   34      Private  216864       HS-grad              9       Divorced   \n",
+       "6   38      Private  150601          10th              6      Separated   \n",
+       "7   74    State-gov   88638     Doctorate             16  Never-married   \n",
+       "8   68  Federal-gov  422013       HS-grad              9       Divorced   \n",
+       "9   41      Private   70037  Some-college             10  Never-married   \n",
+       "\n",
+       "          occupation    relationship   race     sex  capital.gain  \\\n",
+       "0                  ?   Not-in-family  White  Female             0   \n",
+       "1    Exec-managerial   Not-in-family  White  Female             0   \n",
+       "2                  ?       Unmarried  Black  Female             0   \n",
+       "3  Machine-op-inspct       Unmarried  White  Female             0   \n",
+       "4     Prof-specialty       Own-child  White  Female             0   \n",
+       "5      Other-service       Unmarried  White  Female             0   \n",
+       "6       Adm-clerical       Unmarried  White    Male             0   \n",
+       "7     Prof-specialty  Other-relative  White  Female             0   \n",
+       "8     Prof-specialty   Not-in-family  White  Female             0   \n",
+       "9       Craft-repair       Unmarried  White    Male             0   \n",
+       "\n",
+       "   capital.loss  hours.per.week native.country income  \n",
+       "0          4356              40  United-States  <=50K  \n",
+       "1          4356              18  United-States  <=50K  \n",
+       "2          4356              40  United-States  <=50K  \n",
+       "3          3900              40  United-States  <=50K  \n",
+       "4          3900              40  United-States  <=50K  \n",
+       "5          3770              45  United-States  <=50K  \n",
+       "6          3770              40  United-States  <=50K  \n",
+       "7          3683              20  United-States   >50K  \n",
+       "8          3683              40  United-States  <=50K  \n",
+       "9          3004              60              ?   >50K  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(data))\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age               0\n",
+       "workclass         0\n",
+       "fnlwgt            0\n",
+       "education         0\n",
+       "education.num     0\n",
+       "marital.status    0\n",
+       "occupation        0\n",
+       "relationship      0\n",
+       "race              0\n",
+       "sex               0\n",
+       "capital.gain      0\n",
+       "capital.loss      0\n",
+       "hours.per.week    0\n",
+       "native.country    0\n",
+       "income            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age                int64\n",
+       "workclass         object\n",
+       "fnlwgt             int64\n",
+       "education         object\n",
+       "education.num      int64\n",
+       "marital.status    object\n",
+       "occupation        object\n",
+       "relationship      object\n",
+       "race              object\n",
+       "sex               object\n",
+       "capital.gain       int64\n",
+       "capital.loss       int64\n",
+       "hours.per.week     int64\n",
+       "native.country    object\n",
+       "income            object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data['income'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Distribution of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sex distribution\n",
+    "sns.countplot(data['sex'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Age distribution\n",
+    "ages = data['age'].hist(bins=max(data['age'])-min(data['age']))\n",
+    "mean_val = np.mean(data['age'])\n",
+    "plt.axvline(mean_val, linestyle='dashed', linewidth=2, color='yellow', label='mean age')\n",
+    "plt.xlabel('age')\n",
+    "plt.ylabel('count')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['hours.per.week'].hist()\n",
+    "plt.xlabel('hours per week')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, nrows=4, figsize=(20, 20))\n",
+    "plt.subplots_adjust(hspace=0.68)\n",
+    "fig.delaxes(axs[3][1])\n",
+    "\n",
+    "\n",
+    "# Workclass\n",
+    "wc_plot = sns.countplot(data['workclass'], ax=axs[0][0])\n",
+    "wc_plot.set_xticklabels(wc_plot.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "\n",
+    "# Native country\n",
+    "nc_plot = sns.countplot(data['native.country'], ax=axs[0][1])\n",
+    "nc_plot.set_xticklabels(nc_plot.get_xticklabels(), rotation=72, ha=\"right\")\n",
+    "\n",
+    "# Education\n",
+    "order=['Preschool', '1st-4th', '5th-6th', '7th-8th', '9th', '10th', '11th', '12th', 'HS-grad',\n",
+    "       'Some-college', 'Assoc-acdm', 'Assoc-voc', 'Bachelors', 'Masters', 'Prof-school', 'Doctorate']\n",
+    "ed_plot = sns.countplot(data['education'], order=order, ax=axs[1][0])\n",
+    "ed_plot.set_xticklabels(ed_plot.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "\n",
+    "# Marital status\n",
+    "ms_plot = sns.countplot(data['marital.status'], ax=axs[1][1])\n",
+    "ms_plot.set_xticklabels(ms_plot.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "\n",
+    "# Relationship\n",
+    "rel_plot = sns.countplot(data['relationship'], ax=axs[2][0])\n",
+    "rel_plot.set_xticklabels(rel_plot.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "\n",
+    "# Race\n",
+    "race_plot = sns.countplot(data['race'], ax=axs[2][1])\n",
+    "race_plot.set_xticklabels(race_plot.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "\n",
+    "# Occupation\n",
+    "occ_plot = sns.countplot(data['occupation'], ax=axs[3][0])\n",
+    "occ_plot.set_xticklabels(occ_plot.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### How do features relate to one another?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(24, 6))\n",
+    "ro = sns.countplot(data['occupation'], hue=data['sex'])\n",
+    "ro.set_xticklabels(ro.get_xticklabels(), rotation=30, ha=\"right\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 6))\n",
+    "ro = sns.countplot(data['education'], hue=data['sex'], order=order)\n",
+    "ro.set_xticklabels(ro.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "#ro.set_yscale('log')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['income'] = data['income'].map({'<=50K': 0, '>50K': 1}) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How do features relate to income?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n",
+      "catplot is a figure-level function and does not accept target axes. You may wish to try barplot\n",
+      "FixedFormatter should only be used together with FixedLocator\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x2016 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ncols=2, nrows=4, figsize=(24, 28))\n",
+    "#fig.delaxes(axs[3][1])\n",
+    "plt.subplots_adjust(hspace=0.4)\n",
+    "\n",
+    "# education and income\n",
+    "sns.catplot(x=\"education\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", order=order, ax=axs[0][0])\n",
+    "axs[0][0].set_xticklabels(axs[0][0].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[0][0].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "sns.catplot(x=\"workclass\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[0][1])\n",
+    "axs[0][1].set_xticklabels(axs[0][1].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[0][1].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "\n",
+    "sns.catplot(x=\"relationship\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[1][0])\n",
+    "axs[1][0].set_xticklabels(axs[1][0].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[1][0].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "sns.catplot(x=\"marital.status\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[1][1])\n",
+    "axs[1][1].set_xticklabels(axs[1][1].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[1][1].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "sns.catplot(x=\"race\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[2][0])\n",
+    "axs[2][0].set_xticklabels(axs[2][0].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[2][0].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "sns.catplot(x=\"native.country\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[2][1])\n",
+    "axs[2][1].set_xticklabels(axs[2][1].axes.get_xticklabels(), rotation=55, ha=\"right\")\n",
+    "axs[2][1].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "sns.catplot(x=\"sex\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[3][0])\n",
+    "axs[3][0].set_xticklabels(axs[3][0].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[3][0].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "sns.catplot(x=\"occupation\", y=\"income\", data=data, kind=\"bar\", height = 6, palette = \"muted\", ax=axs[3][1])\n",
+    "axs[3][1].set_xticklabels(axs[3][1].axes.get_xticklabels(), rotation=40, ha=\"right\")\n",
+    "axs[3][1].set_ylabel(\">50K probability\")\n",
+    "\n",
+    "#ed_income.set_ylabels(\">50K probability\")\n",
+    "\n",
+    "for i in range(2,10):\n",
+    "        plt.close(i)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Another way of visualizing this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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//l5ffkOSHaatv6SXzVb+U1pr726tLWutLVu8ePEa2xEAAAAAZjfvwVNr7TtJrq+qXXrRM5JcneScJFPfTHd0krP79DlJjurfbrdvktv6LXmfTHJgVW3VHyp+YC8DAAAAYAFYNKHXfUWSM6rqIUm+meTFGUKw91fVS5L8a5Ln97ofT3JwkmuT/Eevm9baLVX1piQX9XontNZumb9dAAAAAGBVJhI8tdYuS7JshkXPmKFuS/KyWbZzSpJT1mjjAAAAAFgjJvGMJwAAAADWA4InAAAAAEYheAIAAABgFHMKnqrqvLmUAQAAAMCUVT5cvKo2SfLQJNtW1VZJqi/aPMn2I7cNAAAAgLXY/X2r3W8meVWSRyW5JPcGTz9IctJ4zQIAAABgbbfK4Km19o4k76iqV7TW/mKe2gQAAADAOuD+RjwlSVprf1FV+yVZOn2d1trpI7ULAAAAgLXcnIKnqvq7JD+T5LIkd/XilkTwBAAAAMCM5hQ8JVmWZNfWWhuzMQAAAACsOzaYY70rk/yXMRsCAAAAwLplriOetk1ydVVdmOTHU4WttUNGaRUAAAAAa725Bk9vHLMRAAAAAKx75vqtdv88dkMAAAAAWLfM9Vvtbs/wLXZJ8pAkGyX5UWtt87EaBgAAAMDaba4jnjabmq6qSnJokn3HahQAAAAAa7+5fqvdPdrgo0meveabAwAAAMC6Yq632v3ytNkNkixLcscoLQIAAABgnTDXb7V73rTpO5N8O8PtdgAAAAAwo7k+4+nFYzcEAAAAgHXLnJ7xVFVLquojVfW9/vOhqloyduMAAAAAWHvN9eHi701yTpJH9Z9/6GUAAAAAMKO5Bk+LW2vvba3d2X9OTbJ4xHYBAAAAsJaba/B0c1X9elVt2H9+PcnNYzYMAAAAgLXbXIOnY5M8P8l3ktyY5PAkx4zUJgAAAADWAXP6VrskJyQ5urV2a5JU1dZJ/jRDIAUAAAAAP2WuI56eMBU6JUlr7ZYkTxynSQAAAACsC+YaPG1QVVtNzfQRT3MdLQUAAADAemiu4dGfJfliVX2gzx+R5M3jNAkAAACAdcGcgqfW2ulVdXGSp/eiX26tXT1eswAAAABY2835drkeNAmbAAAAAJiTuT7jCQAAAAAeEMETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwCsETAAAAAKMQPAEAAAAwiokFT1W1YVUtr6qP9fkdq+rLVXVtVZ1VVQ/p5Rv3+Wv78qXTtvH7vfzrVfXsCe0KAAAAADOY5Iin307y1Wnzb03y9tbaTkluTfKSXv6SJLf28rf3eqmqXZO8IMluSQ5K8ldVteE8tR0AAACA+zGR4KmqliR5bpL39PlK8vQkH+xVTktyWJ8+tM+nL39Gr39okjNbaz9urX0rybVJ9pmXHQAAAADgfk1qxNOfJ/ndJHf3+W2SfL+1dmefX5Fk+z69fZLrk6Qvv63Xv6d8hnUAAAAAmLBF8/2CVfWLSb7XWrukqg6Yp9c8LslxSfLoRz96Pl4S1ri9Xnv6pJswUZeceNSkmwAAAMADNIkRT09NckhVfTvJmRlusXtHki2raioIW5Lkhj59Q5IdkqQv3yLJzdPLZ1jnPlpr726tLWutLVu8ePGa3RsAAAAAZjTvwVNr7fdba0taa0szPBz80621Fyb5TJLDe7Wjk5zdp8/p8+nLP91aa738Bf1b73ZMsnOSC+dpNwAAAAC4H/N+q90q/F6SM6vqD5MsT3JyLz85yd9V1bVJbskQVqW1dlVVvT/J1UnuTPKy1tpd899sAAAAAGYy0eCptXZ+kvP79Dczw7fStdbuSHLELOu/Ocmbx2shAAAAAA/WpL7VDgAAAIB1nOAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFHMe/BUVTtU1Weq6uqquqqqfruXb11V51bVNf33Vr28quqdVXVtVV1eVU+atq2je/1rquro+d4XAAAAAGY3iRFPdyb5ndbarkn2TfKyqto1yeuSnNda2znJeX0+SZ6TZOf+c1ySv06GoCrJG5I8Ock+Sd4wFVYBAAAAMHnzHjy11m5srV3ap29P8tUk2yc5NMlpvdppSQ7r04cmOb0NvpRky6raLsmzk5zbWrultXZrknOTHDR/ewIAAADAqkz0GU9VtTTJE5N8OckjW2s39kXfSfLIPr19kuunrbail81WDgAAAMACMLHgqaoenuRDSV7VWvvB9GWttZakrcHXOq6qLq6qi2+66aY1tVkAAAAAVmEiwVNVbZQhdDqjtfbhXvzdfgtd+u/v9fIbkuwwbfUlvWy28p/SWnt3a21Za23Z4sWL19yOAAAAADCrSXyrXSU5OclXW2tvm7bonCRT30x3dJKzp5Uf1b/dbt8kt/Vb8j6Z5MCq2qo/VPzAXgYAAADAArBoAq/51CQvSnJFVV3Wy/5nkrckeX9VvSTJvyZ5fl/28SQHJ7k2yX8keXGStNZuqao3Jbmo1zuhtXbLvOwBAAAAAPdr3oOn1trnk9Qsi58xQ/2W5GWzbOuUJKesudYBAAAAsKZM9FvtAAAAAFh3CZ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRCJ4AAAAAGIXgCQAAAIBRLJp0AwBY2PZ67emTbsJEXXLiUZNuAgAArLUETw/Q+vwBzIcvAAAA4IFwqx0AAAAAoxA8AQAAADAKwRMAAAAAoxA8AQAAADAKwRMAAAAAoxA8AQAAADAKwRMAAAAAoxA8AQAAADAKwRMAAAAAoxA8AQAAADAKwRMAAAAAoxA8AQAAADCKRZNuAAAAM7vuhN0n3YSJefTxV0y6CQDAGiB4AtYKPnwBAACsfdxqBwAAAMAojHgCAABYQPZ67emTbsLEXHLiUZNuArCGGfEEAAAAwCgETwAAAACMQvAEAAAAwCgETwAAAACMQvAEAAAAwCgETwAAAACMQvAEAAAAwCjW+uCpqg6qqq9X1bVV9bpJtwcAAACAwaJJN2B1VNWGSf4yybOSrEhyUVWd01q7erItWzddd8Luk27CRD36+Csm3QQAAABYq6zVwVOSfZJc21r7ZpJU1ZlJDk0ieAJgjRC6C90BAHjw1vbgafsk10+bX5HkyRNqCwAAAKvBBZ/JXvDZ67WnT/T1J+0jm5046SZM1Fj9r1pro2x4PlTV4UkOaq39tz7/oiRPbq29fKV6xyU5rs/ukuTr89rQdce2Sf590o1gvaX/MSn6HpOk/zEp+h6Tou8xSfrf6nlMa23xyoVr+4inG5LsMG1+SS+7j9bau5O8e74ata6qqotba8sm3Q7WT/ofk6LvMUn6H5Oi7zEp+h6TpP+NY23/VruLkuxcVTtW1UOSvCDJORNuEwAAAABZy0c8tdburKqXJ/lkkg2TnNJau2rCzQIAAAAga3nwlCSttY8n+fik27GecLsik6T/MSn6HpOk/zEp+h6Tou8xSfrfCNbqh4sDAAAAsHCt7c94AgAAAGCBEjytg6rq7VX1qmnzn6yq90yb/7OqOr6qXjfL+j+ch2amqo6pqpPm47WYX1V1V1VdVlVXVdVXqup3qmqDvmxZVb1zAbTxjVX1mkm3g9VXVa/vfe3y3u+ePIE2bFlVL30Q6+mHC1BVtar6s2nzr6mqN06wSWtUVR0y2znAKtb5dlVtO1ab1mW9P/39tPlFVXVTVX1sNbf7qKr64ANc59SqOvwB1D+hqp75wFvHmNbmPrWQVdUBq/s3vJ/tL62qXxtr+wvN2tRPq+qjVfWllcreWFU39HPLy6rqLQ+2zQie1lVfSLJfkvQP+9sm2W3a8v2SfKq15j8PY/l/rbU9W2u7JXlWkuckeUOStNYubq29cnVfoKrW+mfUsfqq6ilJfjHJk1prT0jyzCTXj/Raq+pzWyZ5wMETC9aPk/zyfAUtYx7PVt52VS1qrZ3jHGBe/SjJ46tq0z7/rCQ3PJANzPLv+G+ttVE/8LfWjm+t/dOYr8GDstb2qfXc0iTrTfCUtaSfVtWWSfZKskVVPXalxW/vn2n2bK09oAs23Jfgad10QZKn9OndklyZ5Paq2qqqNk7ys0meMDXaqKp2rKovVtUVVfWHUxupwYlVdWVfdmQv/8uqOqRPf6SqTunTx1bVm/v0r1fVhT0d/puq2rCXv7iqvlFVFyZ56rz8NZio1tr3khyX5OW9Tx1QVR+rqg36FfQtp+pW1TVV9ch+RejTfQTLeVX16L781Kp6V1V9OcmfVNVOVfVPNYyqurSqfqbXe21VXdTX/4Np239973+fT7LLvP4hGMt2Sf69tfbjJGmt/Xtr7d+qaq+q+uequqSGUZ/bJUlVnV9V7+jHpiurap9evk8/Di6vqguqapdefkxVnVNVn05yXlU9vPfJS/tx8dDejrck+Zm+3RP7uvrh2uvODA8XffXKC6pqcVV9qP/bXlRVT72f49lP1e/L31hVf1dVX0jydyu9xjE1XH09t2/35VX1P3r//FJVbd3r/Ubf5lf6azy0l698rFx5/p4Rx6to3zZV9akaRhO+J0mN8Hden3w8yXP79K8med/Uggdw/Fl5fmlVXdnrbljDOdvUMec3e3lV1UlV9fWq+qckj5itgVX1e/249pXqV/Z73zm8qg6qqg9MqzvjyJCqOqIfW79SVZ+dth9n9+PvNVX1hmn1/0evf2X10frT96vP3zPisKpeWVVX9308s5c9rKpOqeG8c/m04/K6bkH3qararqo+W/e+3+7fy39Yw90ZV9Xwfrq4l+/Zj2+X1/D5Yqtefn5VLevT21bVt/v0bnXvZ43Lq2rnXj7jZ5CV2ra0qj5Xw3v5pVW137TFm1fVP/b9e1cNx/cN+/+Fqc9Er+7b+Zmq+kQN5xqfq6rH9fJTq+qd/W//zbp3pM1bkuzf2/ZT7y/rqAXdT7tfTvIPSc5M8oIHsnMLuZ8vOK01P+vgT5JvJXl0kt9M8ltJ3pTk4Axhz+eSHJPkpF73nCRH9emXJflhn/6VJOcm2TDJI5Ncl+FD3guSnNjrXJjkS336vUmenSHY+ockG/Xyv0pyVF/3uiSLkzwkw8iskyb9t/IzSv/74Qxl3+/96IAkH+tl70jy4j795CT/1Kf/IcnRffrYJB/t06cm+ViSDfv8l5P8Up/eJMlDkxyY4QNjZQjXP5bkaRmuZFzR62ye5Nokr5n038rPave1hye5LMk3+rHm55NslCGAX9zrHJnklD59fpK/7dNPS3Jln948yaI+/cwkH+rTxyRZkWTrPr8oyeZ9etvejyrDVcwrp7VLP1yLf5L8sP/7fDvJFklek+SNfdn/SfJzffrRSb7ap2c7ns1W/41JLkmy6Qyvf0zvG5tleM+8Lclv9WVvT/KqPr3NtHX+MMkr+vTKx8qV54/JvecAs7XvnUmO79PPTdKSbDvpf5u18af3pyck+WCG96rLct/3wrkef1aev+e4k+ECz//q0xsnuTjJjhk+UE2dyz0qw3vx4TO08TkZjpsP7fNTr3FqksMzHPuuS/KwXv7XSX59hu1ckWT7Pr3ltHbfmGSbJJtmuCC6LPceDx+W4Vh+VZIn5qePp9P///1bko1X2v4fTbUlw+jTb0y1c139WUv61O8keX2f3jDJZn26JXlhnz4+9x6LLk/y8336hCR/3qfPT7KsT2+b5Nt9+i+mbechvW/N+BlkhrY9NMkmfXrnJBf36QOS3JHksb3N52bo/3slOXfa+lN977wkO/fpJyf59LT/Nx/I8P6/a5Jrp23/Y5PuP/rpT7Xz3CT7J/mvSa6YVv7GDCO0Lus/z16b+vlC+3Gryrrrggy31O2X5G1Jtu/Tt2UIfKZ7aoaQKRmuur61T/9ckve11u5K8t2q+ucke2cIrl5VVbsmuTrJVjWMJnhKklcmOTrDAfqiqkqG/yDfy3BAPr+1dlOSVNVZGf6Ds/46K8PB+L0ZAs2zevlTMrxhJEOf/JNp63ygtXZXVW2W4eT2I0nSWrsjSarqwAwf+pf3+g/PcFKxWZKPtNb+o9c7Z6ydYv601n5YVXtlOGH4hQx96A+TPD7Juf0YtGGGDz1T3tfX/WxVbV7DKJXNkpzWryS1DOHVlHNba7f06UryR1X1tCR3Zzi2PnKGpumHa7nW2g+q6vQM72v/b9qiZybZtfetZLg6/vDMfjybrX6SnNNam77t6T7TWrs9w4jl2zKcZCbDB/Un9OnH1zBSecsMfeyT09b/QH//nm3+/vbnaenH4dbaP1bVrbO0kzlorV1eVUszXPH/+EqLt8jcjj8zzU85MMNo9qmRFVtkOOY8Lfeey/1bHzEwk2cmee/UsWnl12it3VlVn0jyvBqerfLcJL87w3a+kOTUqnp/kg+v1O6bk6SqPpzhHLNlOB7+aFr5/hkuiM7m8iRnVNVHk3x02r4fUvc+L2+T9BB1FdtZ660FfeqiJKdU1UYZLiBe1svvzr3Hx79P8uGq2iJDmPPPvfy0DMHNqnwxyeurakmSD7fWrqmqZ2TmzyAr2yjJSVW1Z5K7ct/PIxe21r6ZJFX1vgx99bwkj62qv0jyj0k+1Y+T+yX5wLTj58bTtvPR1trdSa6uqpnOE9YLC72f9n+bnZN8vrXWquonVfX41trUqMu3t9b+dBW7uJD7+YIieFp3TT3nafcMV5auz5DI/iDDSfHWK9Vvc91wa+2G/kHtoCSf7dt6foZRLrfX8D/gtNba709fr6oOe1B7wlqvhvul78pwUPzZaYu+mGSnPvz0sAyBwf350f29XJI/bq39zUpteNVc28vapZ9UnJ/k/Kq6IsPIzataa0+ZbZUZ5t+U4YP+L/UTpPOnLZ/e516YYQTKXq21n/Sh0JvM8Br64brhz5NcmuF9c8oGSfadCrunVNVsx7PZ6ie9b1XVL6U/By/Jf+u/fzyt+t3T5u/OvedvpyY5rLX2lao6JsOV5CkrHytnO3auqn2sWeck+dMM/07bTCuf6/FnpvkplWHE2yfvU1h18IyVhy9hmDo+HT+HtifDbSgvT3JLhhEit9fwiIXnJkkbnoHyW33bz01ySb8wkMx83J3Nnbnv40CmH2Ofm+ED5fMyfBjbPcO+/0pr7etz3I91yYLtU621c/pFmudmCCPf1lo7fYZV7+8zyPT+cE9faK39nxpuH35uko/XcIvVbJ9BVj7G/mKS7ybZo297+vHvp/pqa+3Wqtojw50dv5Xhc8+rkny/tbbnLO2efgxf3w+oC7afJnlMkq2SfKu/722eISR7/VzWX0j9fKHzjKd11wUZDqq3tNbu6gnxlhlGklywUt0v5N77WV84rfxzSY6s4d7ZxRne6C/sy76U4YD72V7vNf13MlwVOLyqHpEkVbV1VT0mw21RP1/DcyM2SnLEGtpXFrDed96VYYjpfQ66ff4jGUblfXXqamiGPjq9T34uK+kjAVZMBZpVtXENzzf5ZJJjp0YUVNX2vS9+NslhVbVpHy31vDW7p0xCVe3Sr5RN2TPDVe7FNTx4PFW1UVVN/4KFqefV/VyS21prt2W4Qjb1wMtjVvGSWyT5Xg+dfiHDCUuS3J5hNNMU/XAd0N8735/kJdOKP5XkFVMz/Yr5qo5nM9Zf6XU+0u59eOnFD6CJmyW5sb+nvvD+Ks9itvZ9Nv0huFX1nAwn5qyeU5L8QWvtipXK53r8WZVPJvnvvS+kqv5rVT0sw7/j1LncdhlGhqa19uVpfe6cDLeavLjufU7Yyhcok+SfkzwpyW9kCKHSWnv91Hb6ej/Tt318kpuS7NDXfVY/H9w0QzD7hQzv7YdV1UN7W3+pl303ySP6+eLGGc5nU8MX5uzQWvtMkt/rf7epkX6v6Bc+U1VPfJB/w7XRgu1T/dz/u621v03yngx9Jxk+f06NTvm1DCNNbktya/Xn4yR5UYb+lgy3PE8FmPc8ULpf1Pxma+2dSc7OMBJ0xs8gMxxjt0hyYx+R9KIMI6On7FPD8283yHC+8Pkavmhig9bah5L8rwxfaPKDDGHFEf21qodTq7LyucL6YsH20wwh00GttaWttaUZ+tqsz3layP38gf7h5pvgad11RYb7Q7+0UtltrbV/X6nubyd5WR8psP208o9kGNL8lSSfTvK7rbXv9GWfy3BP7rUZrgZv3cvSWrs6w0H5U1V1eYaTme1aazdmuFf2ixlOONbpIdDruU1reNjdVUn+KcMHmz+Ype5ZSX499w5HTYYPQS/u/edFGfroTF6U5JW93gVJ/ktr7VMZnlnyxd6nP5jhfutL+2t8Jcn/zTA0lrXfwzMM076694NdM1zBOjzJW6vqKxnuy5/+4NA7qmp5hkB0KlD4kyR/3MtXNRr4jCTLet86KsnXkqSHDF+o4cGSJ+qH65Q/y/B+OuWVGfrA5VV1dYar31NmOp6tqv7q+t8ZLup8Ib0vPgizte8PkjytH8d/OcPzfVgNrbUV/cPDyuZ6/FmV92R4/MGlNTx092/6tj6S5Jq+7PQM52Azte0TGUYlXFxVl2W4oLhynbsyPCvsOf33TE6s4eHLV2Z4X/5KL78wyYcynFd+qA3fcHtphlF7F2box+9prS1vrf0kw7NPLsxwDjnVtzdM8vf9mLo8yTtba9/PMGpioySX9/76plX+pdYhC7lPZRjd8pXehiMzPAcvGUau7NO3+fQM/9bJ8KiOE/t7+Z7Tyv80Q7CwPPc9Fj8/yZW9vz4+yemzfQaZoW1/leTofo7wuNx3NM1FSU7K8DnlW31/t88wqvqyDLdNTY00eWGSl/TtXJXk0Fn+FlMuT3JXDQ/fX18eLr5g+2kNo6wek2mfl1tr30pyWw0jm+bigCzcfr6g1EoDEABgnVVV52d4mPcDGVUCwINUw22gy1prL590W5i8qvpha+3h918T1l76+U8z4gkAAACAURjxBAAAAMAojHgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwCAEVXVo6rqg316z6o6eA7rHFBVs31l/Uz1/+earAcAsKYIngAARlJVi1pr/9ZaO7wX7ZnkfoOnB2GugZLgCQCYV4InAICVVNXSqvpaVZ1aVd+oqjOq6plV9YWquqaq9uk/X6yq5VV1QVXt0tc9pqrOqapPJzmvb+vKqnpIkhOSHFlVl1XVkbNtYxXt2q6qPtvXv7Kq9q+qtyTZtJed0et9tKouqaqrquq4XnafelPtmrbt11TVG/v0K6vq6qq6vKrOHONvDACsHxZNugEAAAvUTkmOSHJskouS/FqSn0tySIaRQ0cl2b+1dmdVPTPJHyX5lb7uk5I8obV2S1UtTZLW2n9W1fFJlrXWXp4kVbX5KrYxk19L8snW2purasMkD22tfa6qXt5a23NavWP7a2+a5KKq+lBr7XXT6021axavS7Jja+3HVbXlHP5WAAAzEjwBAMzsW621K5Kkqq5Kcl5rrVXVFUmWJtkiyWlVtXOSlmSjaeue21q7ZQ6vsaptzOSiJKdU1UZJPtpau2yWeq+sql/q0zsk2TnJzXNoz5TLk5xRVR9N8tEHsB4AwH241Q4AYGY/njZ997T5uzNcvHtTks+01h6f5HlJNplW/0dzfI1VbeOntNY+m+RpSW5IcmpVHbVynao6IMkzkzyltbZHkuWzbPfO3PdccHqd5yb5ywwjty6qKhcrAYAHRfAEAPDgbJEhAEqSY+a4zu1JNnuw26iqxyT5bmvtb5O8J0MwlCQ/6aOgprZ5a2vtP6rqcUn2nbaJ6fW+m+QRVbVNVW2c5Bf7a2yQZIfW2meS/F7f3sPnuH8AAPcheAIAeHD+JMkfV9XyzP3xBZ9JsuvUw8Xnso2qWlZV7+mzByT5Sq9/ZJJ39PJ3J7m8P1z8E0kWVdVXk7wlyZembe6eeq21n2R42PmFSc5N8rVeZ8Mkf99vKVye5J2tte/Pcf8AAO6jWmuTbgMAAAAA6yAjngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFEIngAAAAAYheAJAAAAgFH8f56T4KfrcOFCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 6))\n",
+    "sns.countplot(data['marital.status'], hue=data['income'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation\n",
+    "\n",
+    "Now the data needs to be prepared for prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['sex'] = data['sex'].map({'Male': 1, 'Female': 0}) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['race'] = data['race'].map({'White': 1, 'Asian-Pac-Islander': 1, 'Black':0, 'Amer-Indian-Eskimo':0, 'Other':0}) \n",
+    "data['relationship'] = data['relationship'].map({'Not-in-family':0, 'Unmarried':0, 'Own-child':0, 'Other-relative':0, 'Husband':1, 'Wife':1})\n",
+    "data['marital.status'] = data['marital.status'].map({'Widowed':0, 'Divorced':0, 'Separated':0, 'Never-married':0, 'Married-civ-spouse':1, 'Married-AF-spouse':1, 'Married-spouse-absent':0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+LSIWFibga3newMysTakqf2reAOD1gteLUl6h2cCp6fgUoEdar382MFJSd0l9gKOBQancGGBKQXwtKu/j0c80knd83jcxM2srgXInSaMlzSpIo1v4dmOB4ZKeAYYDi4G6iJgGTAUeI5tz8jhQJ2kP4IvAL1ryJsX6qC8iazl/KDXdG/QAHm3JG5mZtYWWDM+LiInAxCZOL2ZzKxhgYMorvH4JqUUtaRfgtIhYlc5dBVyVzt0GvAwcCOwDLEjbznaXtCAi9mmunsX6qG8D7ibrIC984vl2RKwscq2ZWdsr3TjqmcAQSXuRBegvk/Urb36rrFtjZUTUA+OASSm/GugVESskDQOGAdNSn/XuBdevLRakoUigjojVwGrgjHTTfkA3YBdJu0TEazk/sJlZm6gv0aiPiKiVNAa4F6gGJkXEPEnjgVkRMQUYAUyQ1LDj1cXp8s7Aw6nVvAY4MwXpbZJ3UaaTgGuBPYBlwJ7AC8A/bOsbm5m1ihJOeImIqWR9zYV5VxQcTwYmN3LderKRH8Xuv0ueeuT9P8L3gU8AL0fEXsCngSdyXmtm1mZKOeGlXOSt6caIWAFUSaqKiAeAg1uxXmZm26Qloz7ai7xrfaxKTzRnALdKWga803rVMjPbNu2ppZxX3k90MrAO+DpwD/BX4KTWqpSZ2TaT8qd2Iu9WXIWt55taqS5mZtutXh1s4wBJb7PlYkxKrwVERNS0Yt3MzFqsErs+io2j7tFWFTEzK4X29JAwr9y/eiQdIemcdNwnzdYxMysrlTg8L++El++QDcfbD/gN0IVsW65PtV7VzMxaroTLnJaNvMPzTiFbTORpyBYikeRuETMrOx3uYWKB9yIi0nx2JO3cinUyM9tmldhHXTRQK1tV5I+S/gPoJekC4FzgV61dOTOzlmpPfc95FQ3UqSX9RbLtZtaQ9VNfERH3tXblzMxaqkO2qJOngVUR8c3WrIyZ2fbqkC3q5DDgK5IWUrDGR55dyCeMbGrzBOvIxt3T0h2PrGN4abvv0JFb1J9t1VqYmZVIff7pIe1G3rU+FrZ2RczMSiE6aqA2M2svOnLXh5lZu+BAbWZW5hyozczKXCUG6srrdTezDq0+qnKnYiSNlPSSpAWSLm/k/J6SpkuaI+lBSQMLzv1I0tyUTi/IvzXdc66kSZI6F6uHA7WZVZRSbW4rqRq4HjgeGAqcIWnoVsWuBm5Oc0rGAxPStScCBwEHkM1DGSupYaOVW4EPAx8FdgLOL/aZHKjNrKKUcBfyQ4EFEfFKRLwH3E62f2yhocD96fiBgvNDgRkRUZu2MpwDjASIiKmRAE8CAynCgdrMKkqEcidJoyXNKkiFU2YHAK8XvF6U8grNBk5Nx6cAPST1TvkjJXWX1Ac4GhhUeGHq8jiLbMPwZvlhoplVlPoWPEyMiInA9qxzMRa4TtIoYAawGKiLiGmSDgEeA5YDjwN1W137S7JW98PF3sSB2swqSp6HhDktZstW8MCUt0lELCG1qCXtApwWEavSuauAq9K524CXG65Lu2b1BS7MUxF3fZhZRSlhH/VMYIikvSR1Ab4MTCkskPaPbYij44BJKb86dYEgaRgwDJiWXp9Ptn7SGRFRn+czuUVtZhUlojTjqCOiVtIY4F6gGpgUEfMkjQdmRcQUYAQwIe1+NQO4OF3eGXg423eFNcCZEVGbzt0ILAQeT+d/HxHjm6uLA7WZVZRSTniJiKnA1K3yrig4ngxMbuS69WQjPxq7Z4vjrgO1mVWUUrWoy4kDtZlVlFydvu2MA7WZVZQSjvooGw7UZlZR3PVhZlbmKnH1PAdqM6so9bGja1B6DtRmVlHcojYzK3PuozYzK3N1DtRmZuXNLWozszIXfphoZlbe/DDRzKzMeXiemVmZq693i9rMrKy1ZCuu9sKB2swqih8mmpmVOQ/PMzMrc36YaGZW5tz1YWZW5jyF3MyszFVii7ry9qwxsw4tIn8qRtJISS9JWiDp8kbO7ylpuqQ5kh6UNLDg3I8kzU3p9IL8vST9Jd3zvyV1KVYPB2ozqyj1odypOZKqgeuB44GhwBmShm5V7Grg5ogYBowHJqRrTwQOAg4ADgPGSqpJ1/wI+GlE7AO8BZxX7DM5UJtZRSlhi/pQYEFEvBIR7wG3AydvVWYocH86fqDg/FBgRkTURsQ7wBxgpCQBxwCTU7mbgC8Uq4gDtZlVlLr6/EnSaEmzCtLoglsNAF4veL0o5RWaDZyajk8BekjqnfJHSuouqQ9wNDAI6A2siojaZu75Pn6YaGYVpSUTXiJiIjBxO95uLHCdpFHADGAxUBcR0yQdAjwGLAceB+q29U0cqM2sopRw1MdislZwg4Epr+C9YgmpRS1pF+C0iFiVzl0FXJXO3Qa8DKwAeknqlFrV77tnY9z1YWYVpT7ypyJmAkPSKI0uwJeBKYUFJPWR1BBHxwGTUn516gJB0jBgGDAtIoKsL/sf0zVnA38oVhEHajOrKKV6mJhavGOAe4EXgDsiYp6k8ZI+n4qNAF6S9DLQn9SCBjoDD0t6nqxr5cyCful/AS6TtICsz/rXxT6Tuz7MrKKUcsJLREwFpm6Vd0XB8WQ2j+AoLLOebORHY/d8hWxESW4O1GZWUerqd3QNSs+B2swqSr0DtZlZeavEtT4cqM2sojhQW5MOO2hXLr1gH6qqxB/ve4PfTX59i/P9+3Zl3KX70aumM2+vrWX8NS+wfMV7AFx09l4cfkhvAH57+0Luf2R5m9ffSm/Yr35AvxNG8N6yFcw48KRGywz96bfoN3I4devWM/u8y1nzzPMADDjrCwwZdxEA8yfcwOJb7myrard7lbhxgIfnlUBVFVz2z0MYe+VznHnxTI49qh+DB3XfosyYc/fmnvuXMuqSp/jN7Qu58OwPAXD4wbux7949OOeSWYz+xtOccepAuu9UvSM+hpXYopt+z5OfO7/J831HHsXO+wzmwY8cx3MX/Rv7X3clAJ137cm+3x7Do5/6Eo988ovs++0xdOpV0+R9bEsRkTu1Fw7UJfCRITUsemMdS5aup7Y2+POMZRxxWO8tygz+YHeenrMKgKfnrOLIdH7woO48O28VdfWwfkM9f331HT7x8d3a+iNYK1j5yCw2rlzd5Pn+n/80i393JwCr/jKbzj1r6Lp7X/oedwTLpz/KxrdWU7tqDcunP0q/zx7ZRrVu/+rq8qf2osWBWlJVwXJ9BvTt3YVlf9+w6fXyFRvo27vrFmUWvLqW4Yf3AeCow/uwc/dO1PToxIK/vcNhB+1G165V9KzpxEHDetGvz5bXWmXqtkd/1i16c9Pr9YvfpNuA/nTboz/rXy/IX7SUbnv03xFVbJdKuR51ucgVqCXdJqlG0s7AXOB5Sd9spvymFaneXHhXqerarl036RUO2L8nk352EAfu35Nlf99AfX0w85m3eOKpldz44wO5cuxQ5r64hrpK7GQzayMlnEJeNvK2qIdGxBqydVPvBvYCzmqqcERMjIiDI+Lg3fds/CFKJVm+4r0tWsF9e3dl+YoNW5RZsfI9vjXhec79f08z8ZZXAVj7TvZ/r5vveI1zLn2Kr18xBwleX7yu7SpvO8z6JUvZaeDum153G7A76xcvZf2SpXQbVJA/sD/rlyzdEVVslzpsixroLKkzWaCeEhEbgXb0MVvXi/PXMGiPnfhA/2506iSOPaofjz65YosyPWs6obT64llf/CB/+nP2X9uqKqjpkQ2+2Xvwzuw9eBdmPrOyTetvO8ayu+5nwJlfAKDXYR+jds3bbHhzOcunPULfY4+gU68aOvWqoe+xR7B82iM7trLtSNRH7tRe5B2e9x/A38gWw54haU9gTWtVqr2pq4drb1zAtd/9KFVV4k9/fpNXX3uX874ymBfnv82jT67gwP17ceHZe0HAs/NWc+0N8wHoVC2u/+EBALz7bh3jr3mhIqfAdkQH3HINvYcfSpc+u3LMqw8xf/wvUOfsn9xrE29n2d0P0ff44Yx48T7q1q1jzvn/CsDGt1Yz/we/5IjHsyUk5l91PRvfavqhpG2pEv/9aFuHqBSsp9qsI056qP382rI2M+6e0cULWYdz4saX8q/634QJd9TljjnjvlS93e/XFnK1qCVd0cSp8SWsi5nZdmtPfc955e36eKfguBvwObL1Wc3MykqHDdQRcU3ha0lXky2mbWZWVuorMFJv61of3cn2+jIzKytRgQ8T8/ZRP8fm4XjVQF/ge61VKTOzbVWX/1liu5G3Rf25guNaYGmeER9mZm2tPS22lFfeCS/fj4iFKS2OiFpJt7RqzczMtkElTiHP26L+h8IXkjoBHy99dczMtk97mnGYV7MtaknjJL0NDJO0JqW3gaXAH9qkhmZmLVDKtT4kjZT0kqQFki5v5PyekqZLmiPpQUkDC879WNI8SS9I+rmULSIh6QxJz6Vr7pHUp1g9mg3UETEhInoAP4mImpR6RETviBhX/GOambWt+vrInZojqRq4HjgeGAqcIWnoVsWuBm6OiGFkEwAnpGs/CXwKGAbsDxwCDE+9Ef8OHJ2umQOMKfaZ8o6jHidpV2AI2YSXhvwZea43M2sr9aUb9XEosCAiXgGQdDtwMvB8QZmhwGXp+AHgznQcZLGyCyCgM1lPhFLaWdIKoAZYUKwiedejPh+YQTbJ5bvpzyvzXGtm1pbqI3KnwrXzUypchGYAULj56aKUV2g2cGo6PgXoIal3RDxOFrjfSOneiHghrTx6EfAcsIQs0P+62GfKO+rjUrKm+8KIOBo4EFiV81ozszbTkj0TC9fOT2liC99uLFmXxjPAcGAxUCdpH+AjZBMDBwDHSDoyLRd9EVkM3YOs66NoN3LeUR/rI2K9JCR1jYgXJe3Xwg9kZtbqivU9t8BiYFDB64Epb5OIWEJqUUvaBTgtIlZJugB4IiLWpnN3A4cD69N1f035dwDve0i5tbwt6kWSepH1v9wn6Q/AwpzXmpm1mRKO+pgJDJG0l6QuwJeBKYUFJPWR1BBHxwGT0vFrpIeHqRU9nGwhu8XAUEl9U7nPkGOBu7wPE09Jh1dKegDoSbYll5lZWakr0c4BaWLfGLJnctXApIiYJ2k8MCsipgAjgAmSguw53sXp8snAMWR90QHcExF3AUj6LtkGLBvJGryjitUl71oft0TEWanyDzXk0cy+iWZmO0IpJ7xExFRg6lZ5VxQcTyYLyltfVwdc2MQ9bwRubEk9tnVmYjWemWhmZcgzEzfPTFyGZyaaWRmqxLU+PDPRzCpKJe5CnnfUxx8l7Qwg6UxJ16adyM3MykpLxlG3F3kD9Q3Au5I+BnwD+Ctwc6vVysxsG9XV1edO7UXeQF0b2a+fk4HrIuJ6oEfrVcvMbNtUYtdH3lEfb0saB5wJHJUGeHduvWqZmW2b9hSA88rboj4d2ACcFxFvkk2l/Emr1crMbBu1ZFGm9iLvzMQ3gWsLXr+G+6jNrAx15Bb1+0hq6SpTZmatrhJHfeTto27Mf5SsFmZmJVJX235Gc+S1zYE6Ip4qZUXMzEqhPbWU82o2UEu6i2zlp0ZFxOdLXiMzs+0Q9R2vRX11m9TCzKxESrhxQNloNlA3LGlqZtZedLiujwaShpBtgz6ULXch/1Ar1cvMbJvUd+CHib8BvgP8FDgaOIftGNpnZtZa6qPyAnXeYLtTREwHFBELI+JK4MTWq5aZ2bbpyGt9bEjre8xPe4gtBnZpvWqZmW2b9hSA88rbor4U6A5cQrYF15nAV1urUmZm26oSZybmDdSDI2JtRCyKiHMi4jTgg61ZMTOzbVFfX587FSNppKSXJC2QdHkj5/eUNF3SHEkPShpYcO7HkuZJekHSzyUp5XeRNFHSy5JelHRasXrkDdSNbbvlrbjMrOzU19XlTs1Jm3hfDxxPNuLtDElDtyp2NXBzRAwDxpONjkPSJ4FPAcOA/YFDgOHpmm8ByyJi33TfosOgi81MPB44ARgg6ecFp2qA2mI3NzNrayXsoz4UWBARrwBIup1s85TnC8oMBS5Lxw8AdzZUg2wocxdAZOv3L03nzgU+DBAR9cDfi1WkWIt6CTALWA88VZCmAJ8tdnMzs7ZWwlEfA4DXC14vSnmFZgOnpuNTgB6SekfE42SB+42U7o2IFyT1SmW/J+lpSf8jqX+xihSbmTgbmC3p1ohwC9rMyl5LxlFLGg2MLsiaGBEtWcJ5LHCdpFHADLIRcXWS9gE+QrbJCsB9ko4EXkh5j0XEZZIuI+s+Oau5NynW9XFHRHwJeEbS+379pH4ZM7Oy0ZKujxSUmwrMi4FBBa8HprzC65eQWtSSdgFOi4hVki4AnoiItenc3cDhwCPAu8Dv0y3+BzivWD2LjaO+NP35uWI3MjMrByVcPW8mMETSXmQB+svAPxUWkNQHWJn6mscBk9Kp14ALJE0g66MeDvwsIiKtSjoCuB/4NFv2eTeqWNfHG+nJ528j4uj8n8/MbMcoNpojr4ioTRP87gWqgUkRMU/SeGBWREwhC7gTUo/DDODidPlk4BjgObIHi/dExF3p3L8At0j6GbCcbEmOZhWdmRgRdZLqJfWMiNUt+JxmZm2ulMucRsRUYOpWeVcUHE8mC8pbX1cHXNjEPRcCR7WkHnmnkK8FnpN0H/BOwRte0pI3MzNrbR1x44AGv2dz57eZWdmqxLU+cgXqiLiptStiZlYKUYHLnHrjADOrKPW1pXmYWE6UZwUpSY+weeOAk0gbBxR2qltxkka3cDC9dQD+Xlgx3jigbY0uXsQ6IH8vrFneOMDMrMx54wAzszKXt0UdwC3AnmTL9QH8imytVcvP/ZDWGH8vrFl5Hya+BHyTbDrkprEvaYaNmZm1orwt6uVpXruZmbWxvC3qTwNnANOBDQ35EeHZimZmrSzvw8RzgAOAkWTjqE+iAy19KmltkfO9JH2t4PUekt63UMt21uFBSQc3kn/wVtuk2Q5Q+DOXdICkE3JcM0LSH1vwHv9aynLWfuTuo46I/dqgPjtM2iFY0cj8U0lrI6LJ4YiSBgN/jIj9W7F+DwJjI2JWa72HbRtJnQp3QEq7fRwcEWOKXDeC7Geaq9FT7HvY0nLWfuRtUT/WyO677Z6kwWkr+JuBucC/SZqZtn7/biPld0lbwz8t6TlJJ6dTPwT2lvSspJ+k+85N13ST9JtU/hlJR6f8UZJ+L+keSfMl/TjlV0v6raS56ZqvF1Thi5KeTNvMH5nKb2qVSbpS0i2SHk/3vKDV/vIqQPo5vZj+vl+WdKukYyU9mv7+Dk3p8fSze0zSfunaUZKmSLofmN7wM5fUhWw36tPT9+H0pu7RTL0+IGlGun6upCMl/RDYKeXdmsrdKekpSfOUbSnF1uUKv4vp/FhJV6bjSyQ9n77vt7fG37GVSEQUTWT7fL0HvATMIRv9MSfPteWcgMFko1g+ARxHNkxKZL/A/ggclcqtTX92AmrScR9gQSo/GJi71X3npuNvkC04DtnOw6+RrZcyCngF6JleLyTb9ufjwH0F9+qV/nwQuCYdnwD8OR2PIGvNA1xJttnmTql+rwN77Oi/53JN6edUC3w0/cyfItuhQ2S7Td8J1ACdUvljgf9Nx6PINjvdrZGf+SjguoL3aeoem352W9XrG8C30nE10KPwe1hQruG9dyJraPTeulwj382xwJXpeAnQtfB75lSeKe+oj5E5y7VHCyPiCUlXkwXrZ1L+LsAQsl0bGgj4gaSjyAL8AKDYDsJHAL8AiIgXJS0E9k3npkfajEHS82Tj1OcBH5L0C+BPwLSCezU8vH2K7B9gY/4QEeuAdZIeINvy/s4idezIXo2I5wAkzSP7mYSk58j+jnsCNylbmCzYPI8Asl+oK3O8R3P3aMxMYJKkzsCdEfFsE+UukXRKOh5E9n1dkaM+DeYAt0q6E39Hylquro/I1vd4X2rtyrWRho0QBEyIiANS2icifr1V2a8AfYGPR8QBwFIKVhPcBhsKjuvIWl1vAR8ja0H/M/CfjZSvo+mhlVs/dKi8xXlLq/BnUF/wup7s7/h7wAORPX84iS1/3u+QT3P3eJ+ImEG2A8hi4LeS3jcLOPVvHwscHhEfI2tgNHbfWrb8d15Y5kTgeuAgYKakvA03a2N5+6g7gnuBc5XtJIykAZL6bVWmJ7AsIjamvuY9U/7bQI8m7vswWYBH0r7AB8m6kBqlbLPMqoj4X+DbZP+IWuLk1C/em+y/1jNbeL1tqSebd54elfOarb8PLbqHpD2BpRHxK7Jf1A3fgY2pld1wz7ci4l1JHybrvqORckuBfpJ6S+pKGq2lbO2eQRHxANkefj3x+j1ly4E6iYhpwG3A4+m/vZN5f/C9FTg4nf8q8GK6dgXwaHrw85OtrvklUJWu+W9gVERsoGkDgAclPQv8jmxn45aYAzwAPAF8L7Lt7G3b/Zhs89JnyD9B7AFgaMPDxDz3UDbMsuF/TyOA2an86cC/p/yJwJz0MPEeoJOkF8geZj9RcLtN5SJiI9nDzSeB+0jfWbK+79+l7+UzwM8jYlXOz2dtLNfwPGsf0tP8tRFx9Y6ui5mVjlvUZmZlzi1qM7My5xa1mVmZc6A2MytzDtRmZmXOgdrMrMw5UJuZlbn/DyMbRXL7N8h6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.heatmap(data[['relationship', 'marital.status']].corr(),annot=True, fmt = \".2f\", cmap = \"coolwarm\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "relationship and marital.status contain the same information now, so one of them can be removed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.drop(['marital.status'], axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "LabelEncoder can be used to transform the rest of the categorical features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data.drop(['workclass', 'education', 'occupation', 'native.country'], axis=1,inplace=True)\n",
+    "\n",
+    "data.drop(['education'], axis=1,inplace=True)\n",
+    "\n",
+    "labels = ['workclass', 'occupation', 'native.country']\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "for l in labels:\n",
+    "    data[l]=le.fit_transform(data[l])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education.num</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital.gain</th>\n",
+       "      <th>capital.loss</th>\n",
+       "      <th>hours.per.week</th>\n",
+       "      <th>native.country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>4</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>4</td>\n",
+       "      <td>264663</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>34</td>\n",
+       "      <td>4</td>\n",
+       "      <td>216864</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3770</td>\n",
+       "      <td>45</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>38</td>\n",
+       "      <td>4</td>\n",
+       "      <td>150601</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3770</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>74</td>\n",
+       "      <td>7</td>\n",
+       "      <td>88638</td>\n",
+       "      <td>16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3683</td>\n",
+       "      <td>20</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>68</td>\n",
+       "      <td>1</td>\n",
+       "      <td>422013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3683</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>41</td>\n",
+       "      <td>4</td>\n",
+       "      <td>70037</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3004</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  workclass  fnlwgt  education.num  occupation  relationship  race  sex  \\\n",
+       "0   90          0   77053              9           0             0     1    0   \n",
+       "1   82          4  132870              9           4             0     1    0   \n",
+       "2   66          0  186061             10           0             0     0    0   \n",
+       "3   54          4  140359              4           7             0     1    0   \n",
+       "4   41          4  264663             10          10             0     1    0   \n",
+       "5   34          4  216864              9           8             0     1    0   \n",
+       "6   38          4  150601              6           1             0     1    1   \n",
+       "7   74          7   88638             16          10             0     1    0   \n",
+       "8   68          1  422013              9          10             0     1    0   \n",
+       "9   41          4   70037             10           3             0     1    1   \n",
+       "\n",
+       "   capital.gain  capital.loss  hours.per.week  native.country  income  \n",
+       "0             0          4356              40              39       0  \n",
+       "1             0          4356              18              39       0  \n",
+       "2             0          4356              40              39       0  \n",
+       "3             0          3900              40              39       0  \n",
+       "4             0          3900              40              39       0  \n",
+       "5             0          3770              45              39       0  \n",
+       "6             0          3770              40              39       0  \n",
+       "7             0          3683              20              39       1  \n",
+       "8             0          3683              40              39       0  \n",
+       "9             0          3004              60               0       1  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset is ready."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Importing the relevant libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, BaggingClassifier, ExtraTreesClassifier\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.model_selection import GridSearchCV, cross_val_score, cross_val_predict, StratifiedKFold, learning_curve, train_test_split, KFold\n",
+    "# from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Preparing data for training and testing with k-fold Cross-Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = 42\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "X = StandardScaler().fit_transform(data.loc[:, data.columns != 'income'])\n",
+    "Y = data['income']\n",
+    "\n",
+    "# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n",
+    "\n",
+    "kf = KFold(n_splits=10, shuffle=True, random_state=seed)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7591904425539756\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = len(data.loc[data.income==0])/len(data)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One would get a 76% accuracy by just always predicting <=50k. Our model has to do better than that or it's not learning anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Starting with some simple models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression\n",
+      "Accuracy score: 0.8425 \n",
+      "\n",
+      "KNeighborsClassifier\n",
+      "Accuracy score: 0.8412 \n",
+      "\n",
+      "LinearDiscriminantAnalysis\n",
+      "Accuracy score: 0.8331 \n",
+      "\n",
+      "GaussianNB\n",
+      "Accuracy score: 0.8033 \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x1008 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, axs = plt.subplots(ncols=2, nrows=2, figsize=(24, 14))\n",
+    "\n",
+    "\n",
+    "classifiers = [\n",
+    "    LogisticRegression(solver='newton-cg'),\n",
+    "    KNeighborsClassifier(n_neighbors=17), # Some trial and error I don't show went into this hyperpa\n",
+    "    LinearDiscriminantAnalysis(),\n",
+    "    GaussianNB()\n",
+    "]\n",
+    "\n",
+    "\n",
+    "for i, c in enumerate(classifiers):\n",
+    "    \n",
+    "    x_axs = i%2\n",
+    "    y_axs = int(i/2)\n",
+    "    # print(c)\n",
+    "    print(type(c).__name__)\n",
+    "    pred = cross_val_predict(c, X, Y, cv=kf)\n",
+    "    print(\"Accuracy score:\", round(accuracy_score(Y, pred), 4), '\\n')\n",
+    "\n",
+    "    sns.heatmap(confusion_matrix(Y, pred), annot=True, fmt='g', ax=axs[y_axs][x_axs])\n",
+    "    axs[y_axs][x_axs].set_xlabel('Predicted')\n",
+    "    axs[y_axs][x_axs].set_ylabel('Real')\n",
+    "    axs[y_axs][x_axs].set_title(type(c).__name__)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Logistic regression performs best with 84.25% accuracy. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More complex models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DecisionTreeClassifier\n",
+      "Accuracy score: 0.8139 \n",
+      "\n",
+      "BaggingClassifier\n",
+      "Accuracy score: 0.8467 \n",
+      "\n",
+      "RandomForestClassifier\n",
+      "Accuracy score: 0.8528 \n",
+      "\n",
+      "ExtraTreesClassifier\n",
+      "Accuracy score: 0.8431 \n",
+      "\n",
+      "GradientBoostingClassifier\n",
+      "Accuracy score: 0.8658 \n",
+      "\n",
+      "AdaBoostClassifier\n",
+      "Accuracy score: 0.8604 \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x1512 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore')\n",
+    "fig, axs = plt.subplots(ncols=2, nrows=3, figsize=(24, 21))\n",
+    "\n",
+    "classifiers = [\n",
+    "    DecisionTreeClassifier(),\n",
+    "    BaggingClassifier(),\n",
+    "    RandomForestClassifier(),\n",
+    "    ExtraTreesClassifier(),\n",
+    "    GradientBoostingClassifier(),\n",
+    "    AdaBoostClassifier()\n",
+    "]\n",
+    "\n",
+    "\n",
+    "for i, c in enumerate(classifiers):\n",
+    "    \n",
+    "    x_axs = i%2\n",
+    "    y_axs = int(i/2)\n",
+    "    \n",
+    "    # print(c)\n",
+    "    print(type(c).__name__)\n",
+    "    pred = cross_val_predict(c, X, Y, cv=kf)\n",
+    "    print(\"Accuracy score:\", round(accuracy_score(Y, pred), 4), '\\n')\n",
+    "    \n",
+    "    sns.heatmap(confusion_matrix(Y, pred), annot=True, fmt='g', ax=axs[y_axs][x_axs])\n",
+    "    axs[y_axs][x_axs].set_xlabel('Predicted')\n",
+    "    axs[y_axs][x_axs].set_ylabel('Real')\n",
+    "    axs[y_axs][x_axs].set_title(type(c).__name__)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradient Boosting with no hyperparameter tuning gets to 86.58% accuracy. Not bad. Let's see if we can do better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Tuning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GridSearchCV allows to try out a lot of hyperparameters at once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "# This takes about 2 hours to run\n",
+    "params = {'max_depth': [5, 6, 7], \n",
+    "         'n_estimators': [100, 150, 200],\n",
+    "          'learning_rate': [0.1, 0.07, 0.05],\n",
+    "          'max_features': ['sqrt', 'log2', 3, 4, 5]\n",
+    "         }\n",
+    "'''\n",
+    "\n",
+    "\n",
+    "params = {'max_depth': [6], \n",
+    "         'n_estimators': [200],\n",
+    "          'learning_rate': [0.07, 0.06],\n",
+    "          'max_features': [3,4]\n",
+    "         }\n",
+    "\n",
+    "classifier = GradientBoostingClassifier()\n",
+    "\n",
+    "grid = GridSearchCV(classifier, param_grid=params, cv=kf)\n",
+    "search_result = grid.fit(X, Y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8733454132244096 with: {'learning_rate': 0.07, 'max_depth': 6, 'max_features': 3, 'n_estimators': 200}\n",
+      "0.8740517797364946 with: {'learning_rate': 0.07, 'max_depth': 6, 'max_features': 4, 'n_estimators': 200}\n",
+      "0.8722397960750591 with: {'learning_rate': 0.06, 'max_depth': 6, 'max_features': 3, 'n_estimators': 200}\n",
+      "0.8733147016369276 with: {'learning_rate': 0.06, 'max_depth': 6, 'max_features': 4, 'n_estimators': 200}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# GridSearch results\n",
+    "means = search_result.cv_results_['mean_test_score']\n",
+    "params = search_result.cv_results_['params']\n",
+    "for m, p in zip(means, params):\n",
+    "    print(f\"{m} with: {p}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
+      "                           learning_rate=0.07, loss='deviance', max_depth=6,\n",
+      "                           max_features=4, 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=200,\n",
+      "                           n_iter_no_change=None, presort='auto',\n",
+      "                           random_state=None, subsample=1.0, tol=0.0001,\n",
+      "                           validation_fraction=0.1, verbose=0,\n",
+      "                           warm_start=False)\n",
+      "Accuracy score: 0.8735 \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = np.argmax(means)\n",
+    "best_param = params[p]\n",
+    "\n",
+    "final_model = GradientBoostingClassifier(**best_param)\n",
+    "\n",
+    "print(final_model)\n",
+    "pred = cross_val_predict(final_model, X, Y, cv=kf)\n",
+    "print(\"Accuracy score:\", round(accuracy_score(Y, pred), 4), '\\n')\n",
+    "\n",
+    "sns.heatmap(confusion_matrix(Y, pred), annot=True, fmt='g')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Final prediction accuracy: 87.35%"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fairness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This DataFrame is created to stock differents models and fair metrics that we produce in this notebook\n",
+    "algo_metrics = pd.DataFrame(columns=['model', 'fair_metrics', 'prediction', 'probs'])\n",
+    "\n",
+    "def add_to_df_algo_metrics(algo_metrics, model, fair_metrics, preds, probs, name):\n",
+    "    return algo_metrics.append(pd.DataFrame(data=[[model, fair_metrics, preds, probs]], columns=['model', 'fair_metrics', 'prediction', 'probs'], index=[name]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fair_metrics(dataset, pred, pred_is_dataset=False):\n",
+    "    if pred_is_dataset:\n",
+    "        dataset_pred = pred\n",
+    "    else:\n",
+    "        dataset_pred = dataset.copy()\n",
+    "        dataset_pred.labels = pred\n",
+    "    \n",
+    "    cols = ['statistical_parity_difference', 'equal_opportunity_difference', 'average_abs_odds_difference',  'disparate_impact', 'theil_index']\n",
+    "    obj_fairness = [[0,0,0,1,0]]\n",
+    "    \n",
+    "    fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols)\n",
+    "    \n",
+    "    for attr in dataset_pred.protected_attribute_names:\n",
+    "        idx = dataset_pred.protected_attribute_names.index(attr)\n",
+    "        privileged_groups =  [{attr:dataset_pred.privileged_protected_attributes[idx][0]}] \n",
+    "        unprivileged_groups = [{attr:dataset_pred.unprivileged_protected_attributes[idx][0]}] \n",
+    "        \n",
+    "        classified_metric = ClassificationMetric(dataset, \n",
+    "                                                     dataset_pred,\n",
+    "                                                     unprivileged_groups=unprivileged_groups,\n",
+    "                                                     privileged_groups=privileged_groups)\n",
+    "\n",
+    "        metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n",
+    "                                                     unprivileged_groups=unprivileged_groups,\n",
+    "                                                     privileged_groups=privileged_groups)\n",
+    "\n",
+    "        acc = classified_metric.accuracy()\n",
+    "\n",
+    "        row = pd.DataFrame([[metric_pred.mean_difference(),\n",
+    "                                classified_metric.equal_opportunity_difference(),\n",
+    "                                classified_metric.average_abs_odds_difference(),\n",
+    "                                metric_pred.disparate_impact(),\n",
+    "                                classified_metric.theil_index()]],\n",
+    "                           columns  = cols,\n",
+    "                           index = [attr]\n",
+    "                          )\n",
+    "        fair_metrics = fair_metrics.append(row)    \n",
+    "    \n",
+    "    fair_metrics = fair_metrics.replace([-np.inf, np.inf], 2)\n",
+    "        \n",
+    "    return fair_metrics\n",
+    "\n",
+    "def plot_fair_metrics(fair_metrics):\n",
+    "    fig, ax = plt.subplots(figsize=(20,4), ncols=5, nrows=1)\n",
+    "\n",
+    "    plt.subplots_adjust(\n",
+    "        left    =  0.125, \n",
+    "        bottom  =  0.1, \n",
+    "        right   =  0.9, \n",
+    "        top     =  0.9, \n",
+    "        wspace  =  .5, \n",
+    "        hspace  =  1.1\n",
+    "    )\n",
+    "\n",
+    "    y_title_margin = 1.2\n",
+    "\n",
+    "    plt.suptitle(\"Fairness metrics\", y = 1.09, fontsize=20)\n",
+    "    sns.set(style=\"dark\")\n",
+    "\n",
+    "    cols = fair_metrics.columns.values\n",
+    "    obj = fair_metrics.loc['objective']\n",
+    "    size_rect = [0.2,0.2,0.2,0.4,0.25]\n",
+    "    rect = [-0.1,-0.1,-0.1,0.8,0]\n",
+    "    bottom = [-1,-1,-1,0,0]\n",
+    "    top = [1,1,1,2,1]\n",
+    "    bound = [[-0.1,0.1],[-0.1,0.1],[-0.1,0.1],[0.8,1.2],[0,0.25]]\n",
+    "\n",
+    "    display(Markdown(\"### Check bias metrics :\"))\n",
+    "    display(Markdown(\"A model can be considered bias if just one of these five metrics show that this model is biased.\"))\n",
+    "    for attr in fair_metrics.index[1:len(fair_metrics)].values:\n",
+    "        display(Markdown(\"#### For the %s attribute :\"%attr))\n",
+    "        check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,5)]\n",
+    "        display(Markdown(\"With default thresholds, bias against unprivileged group detected in **%d** out of 5 metrics\"%(5 - sum(check))))\n",
+    "\n",
+    "    for i in range(0,5):\n",
+    "        plt.subplot(1, 5, i+1)\n",
+    "        ax = sns.barplot(x=fair_metrics.index[1:len(fair_metrics)], y=fair_metrics.iloc[1:len(fair_metrics)][cols[i]])\n",
+    "        \n",
+    "        for j in range(0,len(fair_metrics)-1):\n",
+    "            a, val = ax.patches[j], fair_metrics.iloc[j+1][cols[i]]\n",
+    "            marg = -0.2 if val < 0 else 0.1\n",
+    "            ax.text(a.get_x()+a.get_width()/5, a.get_y()+a.get_height()+marg, round(val, 3), fontsize=15,color='black')\n",
+    "\n",
+    "        plt.ylim(bottom[i], top[i])\n",
+    "        plt.setp(ax.patches, linewidth=0)\n",
+    "        ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor=\"green\", linewidth=1, linestyle='solid'))\n",
+    "        plt.axhline(obj[i], color='black', alpha=0.3)\n",
+    "        plt.title(cols[i])\n",
+    "        ax.set_ylabel('')    \n",
+    "        ax.set_xlabel('')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_fair_metrics_and_plot(data, model, plot=False, model_aif=False):\n",
+    "    pred = model.predict(data).labels if model_aif else model.predict(data.features)\n",
+    "    # fair_metrics function available in the metrics.py file\n",
+    "    fair = fair_metrics(data, pred)\n",
+    "\n",
+    "    if plot:\n",
+    "        # plot_fair_metrics function available in the visualisations.py file\n",
+    "        # The visualisation of this function is inspired by the dashboard on the demo of IBM aif360 \n",
+    "        plot_fair_metrics(fair)\n",
+    "        display(fair)\n",
+    "    \n",
+    "    return fair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education.num</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital.gain</th>\n",
+       "      <th>capital.loss</th>\n",
+       "      <th>hours.per.week</th>\n",
+       "      <th>native.country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>4</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>4</td>\n",
+       "      <td>264663</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32556</th>\n",
+       "      <td>22</td>\n",
+       "      <td>4</td>\n",
+       "      <td>310152</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32557</th>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>257302</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32558</th>\n",
+       "      <td>40</td>\n",
+       "      <td>4</td>\n",
+       "      <td>154374</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32559</th>\n",
+       "      <td>58</td>\n",
+       "      <td>4</td>\n",
+       "      <td>151910</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32560</th>\n",
+       "      <td>22</td>\n",
+       "      <td>4</td>\n",
+       "      <td>201490</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>32561 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age  workclass  fnlwgt  education.num  occupation  relationship  race  \\\n",
+       "0       90          0   77053              9           0             0     1   \n",
+       "1       82          4  132870              9           4             0     1   \n",
+       "2       66          0  186061             10           0             0     0   \n",
+       "3       54          4  140359              4           7             0     1   \n",
+       "4       41          4  264663             10          10             0     1   \n",
+       "...    ...        ...     ...            ...         ...           ...   ...   \n",
+       "32556   22          4  310152             10          11             0     1   \n",
+       "32557   27          4  257302             12          13             1     1   \n",
+       "32558   40          4  154374              9           7             1     1   \n",
+       "32559   58          4  151910              9           1             0     1   \n",
+       "32560   22          4  201490              9           1             0     1   \n",
+       "\n",
+       "       sex  capital.gain  capital.loss  hours.per.week  native.country  income  \n",
+       "0        0             0          4356              40              39       0  \n",
+       "1        0             0          4356              18              39       0  \n",
+       "2        0             0          4356              40              39       0  \n",
+       "3        0             0          3900              40              39       0  \n",
+       "4        0             0          3900              40              39       0  \n",
+       "...    ...           ...           ...             ...             ...     ...  \n",
+       "32556    1             0             0              40              39       0  \n",
+       "32557    0             0             0              38              39       0  \n",
+       "32558    1             0             0              40              39       1  \n",
+       "32559    0             0             0              40              39       0  \n",
+       "32560    1             0             0              20              39       0  \n",
+       "\n",
+       "[32561 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "privileged_groups = [{'sex': 1}]\n",
+    "unprivileged_groups = [{'sex': 0}]\n",
+    "dataset_orig = StandardDataset(data,\n",
+    "                                  label_name='income',\n",
+    "                                  protected_attribute_names=['sex'],\n",
+    "                                  favorable_classes=[1],\n",
+    "                                  privileged_classes=[[1]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "#### Original training dataset"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Difference in mean outcomes between unprivileged and privileged groups = -0.196276\n"
+     ]
+    }
+   ],
+   "source": [
+    "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig, \n",
+    "                                             unprivileged_groups=unprivileged_groups,\n",
+    "                                             privileged_groups=privileged_groups)\n",
+    "display(Markdown(\"#### Original training dataset\"))\n",
+    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipynbname\n",
+    "nb_fname = ipynbname.name()\n",
+    "nb_path = ipynbname.path()\n",
+    "\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "import pickle\n",
+    "\n",
+    "data_orig_train, data_orig_test = dataset_orig.split([0.7], shuffle=True)\n",
+    "X_train = data_orig_train.features\n",
+    "y_train = data_orig_train.labels.ravel()\n",
+    "\n",
+    "X_test = data_orig_test.features\n",
+    "y_test = data_orig_test.labels.ravel()\n",
+    "num_estimators = 100\n",
+    "\n",
+    "model = ExtraTreesClassifier(n_estimators=100)\n",
+    "\n",
+    "\n",
+    "mdl = model.fit(X_train, y_train)\n",
+    "with open('../../Results/ExtraTrees/' + nb_fname + '.pkl', 'wb') as f:\n",
+    "        pickle.dump(mdl, f)\n",
+    "\n",
+    "with open('../../Results/ExtraTrees/' + nb_fname + '_Train' + '.pkl', 'wb') as f:\n",
+    "    pickle.dump(data_orig_train, f) \n",
+    "    \n",
+    "with open('../../Results/ExtraTrees/' + nb_fname + '_Test' + '.pkl', 'wb') as f:\n",
+    "    pickle.dump(data_orig_test, f) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from csv import writer\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "final_metrics = []\n",
+    "accuracy = []\n",
+    "f1= []\n",
+    "\n",
+    "for i in range(1,num_estimators+1):\n",
+    "    \n",
+    "    model = ExtraTreesClassifier(n_estimators=i)\n",
+    "\n",
+    "    \n",
+    "    mdl = model.fit(X_train, y_train)\n",
+    "    yy = mdl.predict(X_test)\n",
+    "    accuracy.append(accuracy_score(y_test, yy))\n",
+    "    f1.append(f1_score(y_test, yy))\n",
+    "    fair = get_fair_metrics_and_plot(data_orig_test, mdl)                           \n",
+    "    fair_list = fair.iloc[1].tolist()\n",
+    "    fair_list.insert(0, i)\n",
+    "    final_metrics.append(fair_list)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0         1         2         3         4         5\n",
+      "0     1 -0.185656 -0.076000  0.087680  0.398240  0.128212\n",
+      "1     2 -0.128241 -0.073642  0.065924  0.313403  0.165647\n",
+      "2     3 -0.193507 -0.113236  0.105257  0.326720  0.124168\n",
+      "3     4 -0.153618 -0.100211  0.082495  0.306796  0.141992\n",
+      "4     5 -0.198680 -0.130253  0.114518  0.306491  0.122249\n",
+      "..  ...       ...       ...       ...       ...       ...\n",
+      "95   96 -0.185504 -0.129355  0.102865  0.303393  0.115864\n",
+      "96   97 -0.188144 -0.115303  0.098114  0.305142  0.114499\n",
+      "97   98 -0.183672 -0.127833  0.101357  0.309476  0.116402\n",
+      "98   99 -0.184449 -0.116904  0.097232  0.310151  0.116170\n",
+      "99  100 -0.184288 -0.120651  0.098502  0.308760  0.115797\n",
+      "\n",
+      "[100 rows x 6 columns]\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>classifier</th>\n",
+       "      <th>T0</th>\n",
+       "      <th>T1</th>\n",
+       "      <th>T2</th>\n",
+       "      <th>T3</th>\n",
+       "      <th>T4</th>\n",
+       "      <th>T5</th>\n",
+       "      <th>T6</th>\n",
+       "      <th>T7</th>\n",
+       "      <th>T8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>T90</th>\n",
+       "      <th>T91</th>\n",
+       "      <th>T92</th>\n",
+       "      <th>T93</th>\n",
+       "      <th>T94</th>\n",
+       "      <th>T95</th>\n",
+       "      <th>T96</th>\n",
+       "      <th>T97</th>\n",
+       "      <th>T98</th>\n",
+       "      <th>T99</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.853107</td>\n",
+       "      <td>0.810625</td>\n",
+       "      <td>0.819326</td>\n",
+       "      <td>0.831098</td>\n",
+       "      <td>0.836421</td>\n",
+       "      <td>0.835398</td>\n",
+       "      <td>0.840311</td>\n",
+       "      <td>0.840925</td>\n",
+       "      <td>0.843484</td>\n",
+       "      <td>0.843792</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.854233</td>\n",
+       "      <td>0.852800</td>\n",
+       "      <td>0.851571</td>\n",
+       "      <td>0.853004</td>\n",
+       "      <td>0.850855</td>\n",
+       "      <td>0.853414</td>\n",
+       "      <td>0.853311</td>\n",
+       "      <td>0.852493</td>\n",
+       "      <td>0.852185</td>\n",
+       "      <td>0.853107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1</th>\n",
+       "      <td>0.669126</td>\n",
+       "      <td>0.609869</td>\n",
+       "      <td>0.528201</td>\n",
+       "      <td>0.634146</td>\n",
+       "      <td>0.600300</td>\n",
+       "      <td>0.641392</td>\n",
+       "      <td>0.619141</td>\n",
+       "      <td>0.647139</td>\n",
+       "      <td>0.629692</td>\n",
+       "      <td>0.655686</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.672493</td>\n",
+       "      <td>0.668358</td>\n",
+       "      <td>0.664972</td>\n",
+       "      <td>0.668360</td>\n",
+       "      <td>0.664518</td>\n",
+       "      <td>0.669284</td>\n",
+       "      <td>0.671707</td>\n",
+       "      <td>0.667436</td>\n",
+       "      <td>0.667587</td>\n",
+       "      <td>0.669126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistical_parity_difference</th>\n",
+       "      <td>-0.184288</td>\n",
+       "      <td>-0.185656</td>\n",
+       "      <td>-0.128241</td>\n",
+       "      <td>-0.193507</td>\n",
+       "      <td>-0.153618</td>\n",
+       "      <td>-0.198680</td>\n",
+       "      <td>-0.165967</td>\n",
+       "      <td>-0.192318</td>\n",
+       "      <td>-0.170593</td>\n",
+       "      <td>-0.198469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.187819</td>\n",
+       "      <td>-0.188265</td>\n",
+       "      <td>-0.185196</td>\n",
+       "      <td>-0.185963</td>\n",
+       "      <td>-0.186590</td>\n",
+       "      <td>-0.185504</td>\n",
+       "      <td>-0.188144</td>\n",
+       "      <td>-0.183672</td>\n",
+       "      <td>-0.184449</td>\n",
+       "      <td>-0.184288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equal_opportunity_difference</th>\n",
+       "      <td>-0.120651</td>\n",
+       "      <td>-0.076000</td>\n",
+       "      <td>-0.073642</td>\n",
+       "      <td>-0.113236</td>\n",
+       "      <td>-0.100211</td>\n",
+       "      <td>-0.130253</td>\n",
+       "      <td>-0.124993</td>\n",
+       "      <td>-0.159293</td>\n",
+       "      <td>-0.118551</td>\n",
+       "      <td>-0.137864</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.129472</td>\n",
+       "      <td>-0.126116</td>\n",
+       "      <td>-0.114563</td>\n",
+       "      <td>-0.121861</td>\n",
+       "      <td>-0.126311</td>\n",
+       "      <td>-0.129355</td>\n",
+       "      <td>-0.115303</td>\n",
+       "      <td>-0.127833</td>\n",
+       "      <td>-0.116904</td>\n",
+       "      <td>-0.120651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_abs_odds_difference</th>\n",
+       "      <td>0.098502</td>\n",
+       "      <td>0.087680</td>\n",
+       "      <td>0.065924</td>\n",
+       "      <td>0.105257</td>\n",
+       "      <td>0.082495</td>\n",
+       "      <td>0.114518</td>\n",
+       "      <td>0.097359</td>\n",
+       "      <td>0.122188</td>\n",
+       "      <td>0.095942</td>\n",
+       "      <td>0.115707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.103759</td>\n",
+       "      <td>0.103321</td>\n",
+       "      <td>0.097201</td>\n",
+       "      <td>0.100187</td>\n",
+       "      <td>0.102882</td>\n",
+       "      <td>0.102865</td>\n",
+       "      <td>0.098114</td>\n",
+       "      <td>0.101357</td>\n",
+       "      <td>0.097232</td>\n",
+       "      <td>0.098502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disparate_impact</th>\n",
+       "      <td>-1.175190</td>\n",
+       "      <td>-0.920700</td>\n",
+       "      <td>-1.160267</td>\n",
+       "      <td>-1.118652</td>\n",
+       "      <td>-1.181572</td>\n",
+       "      <td>-1.182568</td>\n",
+       "      <td>-1.217036</td>\n",
+       "      <td>-1.192051</td>\n",
+       "      <td>-1.233378</td>\n",
+       "      <td>-1.224403</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.198749</td>\n",
+       "      <td>-1.213705</td>\n",
+       "      <td>-1.191567</td>\n",
+       "      <td>-1.197084</td>\n",
+       "      <td>-1.191552</td>\n",
+       "      <td>-1.192725</td>\n",
+       "      <td>-1.186979</td>\n",
+       "      <td>-1.172875</td>\n",
+       "      <td>-1.170696</td>\n",
+       "      <td>-1.175190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theil_index</th>\n",
+       "      <td>0.115797</td>\n",
+       "      <td>0.128212</td>\n",
+       "      <td>0.165647</td>\n",
+       "      <td>0.124168</td>\n",
+       "      <td>0.141992</td>\n",
+       "      <td>0.122249</td>\n",
+       "      <td>0.135048</td>\n",
+       "      <td>0.121722</td>\n",
+       "      <td>0.131349</td>\n",
+       "      <td>0.118503</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.114537</td>\n",
+       "      <td>0.116058</td>\n",
+       "      <td>0.117270</td>\n",
+       "      <td>0.116158</td>\n",
+       "      <td>0.117166</td>\n",
+       "      <td>0.115864</td>\n",
+       "      <td>0.114499</td>\n",
+       "      <td>0.116402</td>\n",
+       "      <td>0.116170</td>\n",
+       "      <td>0.115797</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               classifier        T0        T1        T2  \\\n",
+       "accuracy                         0.853107  0.810625  0.819326  0.831098   \n",
+       "f1                               0.669126  0.609869  0.528201  0.634146   \n",
+       "statistical_parity_difference   -0.184288 -0.185656 -0.128241 -0.193507   \n",
+       "equal_opportunity_difference    -0.120651 -0.076000 -0.073642 -0.113236   \n",
+       "average_abs_odds_difference      0.098502  0.087680  0.065924  0.105257   \n",
+       "disparate_impact                -1.175190 -0.920700 -1.160267 -1.118652   \n",
+       "theil_index                      0.115797  0.128212  0.165647  0.124168   \n",
+       "\n",
+       "                                     T3        T4        T5        T6  \\\n",
+       "accuracy                       0.836421  0.835398  0.840311  0.840925   \n",
+       "f1                             0.600300  0.641392  0.619141  0.647139   \n",
+       "statistical_parity_difference -0.153618 -0.198680 -0.165967 -0.192318   \n",
+       "equal_opportunity_difference  -0.100211 -0.130253 -0.124993 -0.159293   \n",
+       "average_abs_odds_difference    0.082495  0.114518  0.097359  0.122188   \n",
+       "disparate_impact              -1.181572 -1.182568 -1.217036 -1.192051   \n",
+       "theil_index                    0.141992  0.122249  0.135048  0.121722   \n",
+       "\n",
+       "                                     T7        T8  ...       T90       T91  \\\n",
+       "accuracy                       0.843484  0.843792  ...  0.854233  0.852800   \n",
+       "f1                             0.629692  0.655686  ...  0.672493  0.668358   \n",
+       "statistical_parity_difference -0.170593 -0.198469  ... -0.187819 -0.188265   \n",
+       "equal_opportunity_difference  -0.118551 -0.137864  ... -0.129472 -0.126116   \n",
+       "average_abs_odds_difference    0.095942  0.115707  ...  0.103759  0.103321   \n",
+       "disparate_impact              -1.233378 -1.224403  ... -1.198749 -1.213705   \n",
+       "theil_index                    0.131349  0.118503  ...  0.114537  0.116058   \n",
+       "\n",
+       "                                    T92       T93       T94       T95  \\\n",
+       "accuracy                       0.851571  0.853004  0.850855  0.853414   \n",
+       "f1                             0.664972  0.668360  0.664518  0.669284   \n",
+       "statistical_parity_difference -0.185196 -0.185963 -0.186590 -0.185504   \n",
+       "equal_opportunity_difference  -0.114563 -0.121861 -0.126311 -0.129355   \n",
+       "average_abs_odds_difference    0.097201  0.100187  0.102882  0.102865   \n",
+       "disparate_impact              -1.191567 -1.197084 -1.191552 -1.192725   \n",
+       "theil_index                    0.117270  0.116158  0.117166  0.115864   \n",
+       "\n",
+       "                                    T96       T97       T98       T99  \n",
+       "accuracy                       0.853311  0.852493  0.852185  0.853107  \n",
+       "f1                             0.671707  0.667436  0.667587  0.669126  \n",
+       "statistical_parity_difference -0.188144 -0.183672 -0.184449 -0.184288  \n",
+       "equal_opportunity_difference  -0.115303 -0.127833 -0.116904 -0.120651  \n",
+       "average_abs_odds_difference    0.098114  0.101357  0.097232  0.098502  \n",
+       "disparate_impact              -1.186979 -1.172875 -1.170696 -1.175190  \n",
+       "theil_index                    0.114499  0.116402  0.116170  0.115797  \n",
+       "\n",
+       "[7 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "final_result = pd.DataFrame(final_metrics)\n",
+    "print(final_result)\n",
+    "final_result[4] = np.log(final_result[4])\n",
+    "final_result = final_result.transpose()\n",
+    "final_result.loc[0] = f1  # add f1 and acc to df\n",
+    "acc = pd.DataFrame(accuracy).transpose()\n",
+    "acc = acc.rename(index={0: 'accuracy'})\n",
+    "final_result = pd.concat([acc,final_result])\n",
+    "final_result = final_result.rename(index={0: 'f1', 1: 'statistical_parity_difference', 2: 'equal_opportunity_difference', 3: 'average_abs_odds_difference', 4: 'disparate_impact', 5: 'theil_index'})\n",
+    "final_result.columns = ['T' + str(col) for col in final_result.columns]\n",
+    "final_result.insert(0, \"classifier\", final_result['T' + str(num_estimators - 1)])   ##Add final metrics add the beginning of the df\n",
+    "final_result.to_csv('../../Results/ExtraTrees/' + nb_fname + '.csv')\n",
+    "final_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}