diff --git "a/AdultNoteBook/Kernels/ExtraTrees/4-a-simple-knn-application.ipynb" "b/AdultNoteBook/Kernels/ExtraTrees/4-a-simple-knn-application.ipynb"
new file mode 100644--- /dev/null
+++ "b/AdultNoteBook/Kernels/ExtraTrees/4-a-simple-knn-application.ipynb"
@@ -0,0 +1,1873 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KNN "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "from sklearn.ensemble import ExtraTreesClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": [
+    "# 2. Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </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",
+       "\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",
+       "\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  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv('../../Data/adult.csv')\n",
+    "display(dataset.head(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>fnlwgt</th>\n",
+       "      <th>education.num</th>\n",
+       "      <th>capital.gain</th>\n",
+       "      <th>capital.loss</th>\n",
+       "      <th>hours.per.week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>32561.000000</td>\n",
+       "      <td>3.256100e+04</td>\n",
+       "      <td>32561.000000</td>\n",
+       "      <td>32561.000000</td>\n",
+       "      <td>32561.000000</td>\n",
+       "      <td>32561.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>38.581647</td>\n",
+       "      <td>1.897784e+05</td>\n",
+       "      <td>10.080679</td>\n",
+       "      <td>1077.648844</td>\n",
+       "      <td>87.303830</td>\n",
+       "      <td>40.437456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>13.640433</td>\n",
+       "      <td>1.055500e+05</td>\n",
+       "      <td>2.572720</td>\n",
+       "      <td>7385.292085</td>\n",
+       "      <td>402.960219</td>\n",
+       "      <td>12.347429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>1.228500e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>1.178270e+05</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>1.783560e+05</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>2.370510e+05</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>45.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.484705e+06</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>99999.000000</td>\n",
+       "      <td>4356.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                age        fnlwgt  education.num  capital.gain  capital.loss  \\\n",
+       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
+       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
+       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
+       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
+       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
+       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
+       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
+       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
+       "\n",
+       "       hours.per.week  \n",
+       "count    32561.000000  \n",
+       "mean        40.437456  \n",
+       "std         12.347429  \n",
+       "min          1.000000  \n",
+       "25%         40.000000  \n",
+       "50%         40.000000  \n",
+       "75%         45.000000  \n",
+       "max         99.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 32561 entries, 0 to 32560\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   age             32561 non-null  int64 \n",
+      " 1   workclass       32561 non-null  object\n",
+      " 2   fnlwgt          32561 non-null  int64 \n",
+      " 3   education       32561 non-null  object\n",
+      " 4   education.num   32561 non-null  int64 \n",
+      " 5   marital.status  32561 non-null  object\n",
+      " 6   occupation      32561 non-null  object\n",
+      " 7   relationship    32561 non-null  object\n",
+      " 8   race            32561 non-null  object\n",
+      " 9   sex             32561 non-null  object\n",
+      " 10  capital.gain    32561 non-null  int64 \n",
+      " 11  capital.loss    32561 non-null  int64 \n",
+      " 12  hours.per.week  32561 non-null  int64 \n",
+      " 13  native.country  32561 non-null  object\n",
+      " 14  income          32561 non-null  object\n",
+      "dtypes: int64(6), object(9)\n",
+      "memory usage: 3.7+ MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(dataset.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 Analising columns values\n",
+    "No line seems to have Nan or empty values. However futher investigation is needed once it's already possible to see '?' values in different columns. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Work class categories \n",
+      "\n",
+      "['?' 'Private' 'State-gov' 'Federal-gov' 'Self-emp-not-inc' 'Self-emp-inc'\n",
+      " 'Local-gov' 'Without-pay' 'Never-worked']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Work class categories \\n\")\n",
+    "print(dataset['workclass'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Education categories\n",
+      "                  education\n",
+      "education.num              \n",
+      "1                 Preschool\n",
+      "2                   1st-4th\n",
+      "3                   5th-6th\n",
+      "4                   7th-8th\n",
+      "5                       9th\n",
+      "6                      10th\n",
+      "7                      11th\n",
+      "8                      12th\n",
+      "9                   HS-grad\n",
+      "10             Some-college\n",
+      "11                Assoc-voc\n",
+      "12               Assoc-acdm\n",
+      "13                Bachelors\n",
+      "14                  Masters\n",
+      "15              Prof-school\n",
+      "16                Doctorate\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Education categories\")\n",
+    "education_dataset = dataset[['education','education.num']]\n",
+    "education_dataset = education_dataset.drop_duplicates()\n",
+    "\n",
+    "data = {'education': education_dataset['education'], 'education.num': education_dataset['education.num']}\n",
+    "\n",
+    "education_dataset = pd.DataFrame(data=data)\n",
+    "education_dataset['education'].astype('category')\n",
+    "education_dataset.index = education_dataset['education.num']\n",
+    "print(education_dataset[['education']].sort_values('education.num'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The columns 'education' and 'education.num' represent the same information. 'education.num' is the respective label for an education level. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "marital status\n",
+      "['Widowed' 'Divorced' 'Separated' 'Never-married' 'Married-civ-spouse'\n",
+      " 'Married-spouse-absent' 'Married-AF-spouse']\n",
+      " \n",
+      " occupation\n",
+      "['?' 'Exec-managerial' 'Machine-op-inspct' 'Prof-specialty'\n",
+      " 'Other-service' 'Adm-clerical' 'Craft-repair' 'Transport-moving'\n",
+      " 'Handlers-cleaners' 'Sales' 'Farming-fishing' 'Tech-support'\n",
+      " 'Protective-serv' 'Armed-Forces' 'Priv-house-serv']\n",
+      " \n",
+      " relationship\n",
+      "['Not-in-family' 'Unmarried' 'Own-child' 'Other-relative' 'Husband' 'Wife']\n",
+      " \n",
+      " race\n",
+      "['White' 'Black' 'Asian-Pac-Islander' 'Other' 'Amer-Indian-Eskimo']\n",
+      " \n",
+      " native.country\n",
+      "['United-States' '?' 'Mexico' 'Greece' 'Vietnam' 'China' 'Taiwan' 'India'\n",
+      " 'Philippines' 'Trinadad&Tobago' 'Canada' 'South' 'Holand-Netherlands'\n",
+      " 'Puerto-Rico' 'Poland' 'Iran' 'England' 'Germany' 'Italy' 'Japan' 'Hong'\n",
+      " 'Honduras' 'Cuba' 'Ireland' 'Cambodia' 'Peru' 'Nicaragua'\n",
+      " 'Dominican-Republic' 'Haiti' 'El-Salvador' 'Hungary' 'Columbia'\n",
+      " 'Guatemala' 'Jamaica' 'Ecuador' 'France' 'Yugoslavia' 'Scotland'\n",
+      " 'Portugal' 'Laos' 'Thailand' 'Outlying-US(Guam-USVI-etc)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('marital status')\n",
+    "print(dataset['marital.status'].unique())\n",
+    "print(' \\n occupation')\n",
+    "print(dataset['occupation'].unique())\n",
+    "print(' \\n relationship')\n",
+    "print(dataset['relationship'].unique())\n",
+    "print(' \\n race')\n",
+    "print(dataset['race'].unique())\n",
+    "print(' \\n native.country')\n",
+    "print(dataset['native.country'].unique())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Dataset cleaning\n",
+    "\n",
+    "As mentioned before, in many columns are present the '?' value for a missing information. So it will be removed every observation that contains a missing value. Also, it will be removed the columns 'capital.gain' and 'capital.loss', which doesn't seem so clear its meaning, and 'fnlwgt' which represents some final avaliation about the observation, thus the fitting process will get an equal result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Replacing ? for a nan value to drop every line with it\n",
+    "dataset = dataset.replace({'?': np.nan})\n",
+    "dataset = dataset.dropna()\n",
+    "dataset = dataset.drop(['fnlwgt', 'capital.gain','capital.loss'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "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"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "ax = dataset['sex'].value_counts().plot(kind=\"bar\")\n",
+    "ax.set_ylabel(\"Quantity\")\n",
+    "plt.title(\"Sex quantities\")\n",
+    "plt.show()\n",
+    "\n",
+    "ax = dataset['age'].hist()\n",
+    "ax.set_xlabel(\"Age\")\n",
+    "ax.set_ylabel(\"Quantity\")\n",
+    "plt.title(\"Age quantities\")\n",
+    "plt.show()\n",
+    "\n",
+    "ax = dataset['education.num'].hist()\n",
+    "ax.set_xlabel(\"Education label\")\n",
+    "ax.set_ylabel(\"Quantity\")\n",
+    "plt.title(\"Education level quantities\")\n",
+    "plt.show()\n",
+    "\n",
+    "ax = dataset['race'].value_counts().plot(kind=\"bar\")\n",
+    "ax.set_ylabel(\"Quantity\")\n",
+    "plt.title(\"Race quantities\")\n",
+    "plt.show()\n",
+    "\n",
+    "dataset['native.country'].value_counts().plot(kind=\"bar\")\n",
+    "ax.set_ylabel(\"Quantity\")\n",
+    "plt.title(\"Countries quantities\")\n",
+    "plt.show()\n",
+    "\n",
+    "dataset['income'].value_counts().plot(kind=\"bar\")\n",
+    "ax.set_ylabel(\"Quantity\")\n",
+    "plt.title(\"Income quantities\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Fitting a model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(30162, 11) (30162,)\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>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>hours.per.week</th>\n",
+       "      <th>native.country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>38</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age  workclass  education  education.num  marital.status  occupation  \\\n",
+       "1   82          2         11              9               6           3   \n",
+       "3   54          2          5              4               0           6   \n",
+       "4   41          2         15             10               5           9   \n",
+       "5   34          2         11              9               0           7   \n",
+       "6   38          2          0              6               5           0   \n",
+       "\n",
+       "   relationship  race  sex  hours.per.week  native.country  \n",
+       "1             1     4    0              18              38  \n",
+       "3             4     4    0              40              38  \n",
+       "4             3     4    0              40              38  \n",
+       "5             4     4    0              45              38  \n",
+       "6             4     4    1              40              38  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Preparing the features and target\n",
+    "features = dataset.drop(\"income\", axis=1)\n",
+    "target = dataset.income\n",
+    "\n",
+    "#encoding the category features\n",
+    "features_to_encode = features[['workclass', 'education', 'marital.status',\n",
+    "       'occupation', 'relationship', 'race', 'sex',\n",
+    "       'native.country']]\n",
+    "\n",
+    "features_encoded = features_to_encode.apply(preprocessing.LabelEncoder().fit_transform)\n",
+    "target = preprocessing.LabelEncoder().fit_transform(target)\n",
+    "features[['workclass', 'education', 'marital.status',\n",
+    "       'occupation', 'relationship', 'race', 'sex',\n",
+    "       'native.country']] = features_encoded\n",
+    "\n",
+    "print(features.shape, target.shape)\n",
+    "\n",
+    "display(features.head(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Dividing train and test data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(features,target, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>26.506240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hours.per.week</th>\n",
+       "      <td>14.746229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education.num</th>\n",
+       "      <td>10.760729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>occupation</th>\n",
+       "      <td>10.709608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>relationship</th>\n",
+       "      <td>10.678339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>marital.status</th>\n",
+       "      <td>8.740089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>workclass</th>\n",
+       "      <td>5.967166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <td>4.437990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <td>3.332022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>native.country</th>\n",
+       "      <td>2.164723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>race</th>\n",
+       "      <td>1.956864</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                importance\n",
+       "age              26.506240\n",
+       "hours.per.week   14.746229\n",
+       "education.num    10.760729\n",
+       "occupation       10.709608\n",
+       "relationship     10.678339\n",
+       "marital.status    8.740089\n",
+       "workclass         5.967166\n",
+       "education         4.437990\n",
+       "sex               3.332022\n",
+       "native.country    2.164723\n",
+       "race              1.956864"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Analising the % importance level in each feature\n",
+    "forest = ExtraTreesClassifier(n_estimators=250,random_state=0)\n",
+    "forest.fit(features, target)\n",
+    "importances = forest.feature_importances_\n",
+    "feature_importances = pd.DataFrame(importances*100,index = X_train.columns,columns=['importance']).sort_values('importance', ascending=False)\n",
+    "display(feature_importances)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Analisng the accuracy by increasing the number of K\n",
+    "scores = []\n",
+    "for i in range(1,30):\n",
+    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
+    "    knn.fit(X_train, y_train)\n",
+    "    y_pred = knn.predict(X_test)\n",
+    "    scores.append(metrics.accuracy_score(y_test, y_pred))\n",
+    "\n",
+    "sns.lineplot(range(1,30), scores)\n",
+    "plt.xlabel('Value of K for KNN')\n",
+    "plt.ylabel('Testing Accuracy')\n",
+    "plt.title(\"Respective accuracy when increased the number of K\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best K value in this dataset is 13 - Accuracy = 0.7999778981102884\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The best K value in this dataset is {0} - Accuracy = {1}\".format(scores.index(max(scores)), max(scores)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fairness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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": 15,
+   "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": 16,
+   "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": 18,
+   "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>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>hours.per.week</th>\n",
+       "      <th>native.country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>38</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32556</th>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32557</th>\n",
+       "      <td>27</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32558</th>\n",
+       "      <td>40</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32559</th>\n",
+       "      <td>58</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32560</th>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>30162 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age  workclass  education  education.num  marital.status  occupation  \\\n",
+       "1       82          2         11              9               6           3   \n",
+       "3       54          2          5              4               0           6   \n",
+       "4       41          2         15             10               5           9   \n",
+       "5       34          2         11              9               0           7   \n",
+       "6       38          2          0              6               5           0   \n",
+       "...    ...        ...        ...            ...             ...         ...   \n",
+       "32556   22          2         15             10               4          10   \n",
+       "32557   27          2          7             12               2          12   \n",
+       "32558   40          2         11              9               2           6   \n",
+       "32559   58          2         11              9               6           0   \n",
+       "32560   22          2         11              9               4           0   \n",
+       "\n",
+       "       relationship  race  sex  hours.per.week  native.country  income  \n",
+       "1                 1     4    0              18              38       0  \n",
+       "3                 4     4    0              40              38       0  \n",
+       "4                 3     4    0              40              38       0  \n",
+       "5                 4     4    0              45              38       0  \n",
+       "6                 4     4    1              40              38       0  \n",
+       "...             ...   ...  ...             ...             ...     ...  \n",
+       "32556             1     4    1              40              38       0  \n",
+       "32557             5     4    0              38              38       0  \n",
+       "32558             0     4    1              40              38       1  \n",
+       "32559             4     4    0              40              38       0  \n",
+       "32560             3     4    1              20              38       0  \n",
+       "\n",
+       "[30162 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset['income']=dataset['income'].map({'<=50K': 0, '>50K': 1, '<=50K.': 0, '>50K.': 1})\n",
+    "features['income'] = dataset.income\n",
+    "features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "privileged_groups = [{'sex': 1}]\n",
+    "unprivileged_groups = [{'sex': 0}]\n",
+    "dataset_orig = StandardDataset(features,\n",
+    "                                  label_name='income',\n",
+    "                                  protected_attribute_names=['sex'],\n",
+    "                                  favorable_classes=[1],\n",
+    "                                  privileged_classes=[[1]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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 = -0.200159\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())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipynbname\n",
+    "nb_fname = ipynbname.name()\n",
+    "nb_path = ipynbname.path()\n",
+    "\n",
+    "from xgboost import XGBClassifier\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 = 250\n",
+    "\n",
+    "model = ExtraTreesClassifier(n_estimators=250,random_state=0)\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": 22,
+   "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, random_state=0)\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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       0         1         2         3         4         5\n",
+      "0      1 -0.193717 -0.088546  0.107385  0.358240  0.156897\n",
+      "1      2 -0.158251 -0.105207  0.099070  0.305132  0.177942\n",
+      "2      3 -0.188583 -0.097876  0.105711  0.345752  0.153081\n",
+      "3      4 -0.170619 -0.085252  0.093289  0.325594  0.164190\n",
+      "4      5 -0.187544 -0.062743  0.090209  0.350834  0.151774\n",
+      "..   ...       ...       ...       ...       ...       ...\n",
+      "245  246 -0.190805 -0.105171  0.108043  0.324989  0.149173\n",
+      "246  247 -0.191297 -0.106214  0.108684  0.324424  0.148949\n",
+      "247  248 -0.190313 -0.105171  0.107685  0.325556  0.149081\n",
+      "248  249 -0.191461 -0.107256  0.109086  0.324237  0.148663\n",
+      "249  250 -0.191483 -0.111895  0.111166  0.322591  0.149112\n",
+      "\n",
+      "[250 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>T240</th>\n",
+       "      <th>T241</th>\n",
+       "      <th>T242</th>\n",
+       "      <th>T243</th>\n",
+       "      <th>T244</th>\n",
+       "      <th>T245</th>\n",
+       "      <th>T246</th>\n",
+       "      <th>T247</th>\n",
+       "      <th>T248</th>\n",
+       "      <th>T249</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.804398</td>\n",
+       "      <td>0.781744</td>\n",
+       "      <td>0.791469</td>\n",
+       "      <td>0.794784</td>\n",
+       "      <td>0.797436</td>\n",
+       "      <td>0.796000</td>\n",
+       "      <td>0.801746</td>\n",
+       "      <td>0.801746</td>\n",
+       "      <td>0.802409</td>\n",
+       "      <td>0.803846</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.804177</td>\n",
+       "      <td>0.804398</td>\n",
+       "      <td>0.804288</td>\n",
+       "      <td>0.804288</td>\n",
+       "      <td>0.803956</td>\n",
+       "      <td>0.804177</td>\n",
+       "      <td>0.804288</td>\n",
+       "      <td>0.804509</td>\n",
+       "      <td>0.804619</td>\n",
+       "      <td>0.804398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1</th>\n",
+       "      <td>0.584897</td>\n",
+       "      <td>0.554277</td>\n",
+       "      <td>0.511772</td>\n",
+       "      <td>0.570437</td>\n",
+       "      <td>0.548188</td>\n",
+       "      <td>0.573869</td>\n",
+       "      <td>0.566248</td>\n",
+       "      <td>0.582985</td>\n",
+       "      <td>0.570605</td>\n",
+       "      <td>0.584795</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.585012</td>\n",
+       "      <td>0.584702</td>\n",
+       "      <td>0.584954</td>\n",
+       "      <td>0.584565</td>\n",
+       "      <td>0.584543</td>\n",
+       "      <td>0.584623</td>\n",
+       "      <td>0.585149</td>\n",
+       "      <td>0.585034</td>\n",
+       "      <td>0.585948</td>\n",
+       "      <td>0.584897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistical_parity_difference</th>\n",
+       "      <td>-0.191483</td>\n",
+       "      <td>-0.193717</td>\n",
+       "      <td>-0.158251</td>\n",
+       "      <td>-0.188583</td>\n",
+       "      <td>-0.170619</td>\n",
+       "      <td>-0.187544</td>\n",
+       "      <td>-0.174016</td>\n",
+       "      <td>-0.186649</td>\n",
+       "      <td>-0.174081</td>\n",
+       "      <td>-0.182725</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.190958</td>\n",
+       "      <td>-0.189646</td>\n",
+       "      <td>-0.190466</td>\n",
+       "      <td>-0.190313</td>\n",
+       "      <td>-0.191461</td>\n",
+       "      <td>-0.190805</td>\n",
+       "      <td>-0.191297</td>\n",
+       "      <td>-0.190313</td>\n",
+       "      <td>-0.191461</td>\n",
+       "      <td>-0.191483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equal_opportunity_difference</th>\n",
+       "      <td>-0.111895</td>\n",
+       "      <td>-0.088546</td>\n",
+       "      <td>-0.105207</td>\n",
+       "      <td>-0.097876</td>\n",
+       "      <td>-0.085252</td>\n",
+       "      <td>-0.062743</td>\n",
+       "      <td>-0.068929</td>\n",
+       "      <td>-0.071837</td>\n",
+       "      <td>-0.060978</td>\n",
+       "      <td>-0.066391</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.106214</td>\n",
+       "      <td>-0.104650</td>\n",
+       "      <td>-0.105692</td>\n",
+       "      <td>-0.104650</td>\n",
+       "      <td>-0.105692</td>\n",
+       "      <td>-0.105171</td>\n",
+       "      <td>-0.106214</td>\n",
+       "      <td>-0.105171</td>\n",
+       "      <td>-0.107256</td>\n",
+       "      <td>-0.111895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_abs_odds_difference</th>\n",
+       "      <td>0.111166</td>\n",
+       "      <td>0.107385</td>\n",
+       "      <td>0.099070</td>\n",
+       "      <td>0.105711</td>\n",
+       "      <td>0.093289</td>\n",
+       "      <td>0.090209</td>\n",
+       "      <td>0.085960</td>\n",
+       "      <td>0.092036</td>\n",
+       "      <td>0.082052</td>\n",
+       "      <td>0.087186</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.108492</td>\n",
+       "      <td>0.107112</td>\n",
+       "      <td>0.107992</td>\n",
+       "      <td>0.107544</td>\n",
+       "      <td>0.108663</td>\n",
+       "      <td>0.108043</td>\n",
+       "      <td>0.108684</td>\n",
+       "      <td>0.107685</td>\n",
+       "      <td>0.109086</td>\n",
+       "      <td>0.111166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disparate_impact</th>\n",
+       "      <td>-1.131371</td>\n",
+       "      <td>-1.026553</td>\n",
+       "      <td>-1.187010</td>\n",
+       "      <td>-1.062033</td>\n",
+       "      <td>-1.122103</td>\n",
+       "      <td>-1.047442</td>\n",
+       "      <td>-1.084968</td>\n",
+       "      <td>-1.061997</td>\n",
+       "      <td>-1.062977</td>\n",
+       "      <td>-1.050385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.122019</td>\n",
+       "      <td>-1.117376</td>\n",
+       "      <td>-1.120281</td>\n",
+       "      <td>-1.122222</td>\n",
+       "      <td>-1.126281</td>\n",
+       "      <td>-1.123964</td>\n",
+       "      <td>-1.125703</td>\n",
+       "      <td>-1.122222</td>\n",
+       "      <td>-1.126281</td>\n",
+       "      <td>-1.131371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theil_index</th>\n",
+       "      <td>0.149112</td>\n",
+       "      <td>0.156897</td>\n",
+       "      <td>0.177942</td>\n",
+       "      <td>0.153081</td>\n",
+       "      <td>0.164190</td>\n",
+       "      <td>0.151774</td>\n",
+       "      <td>0.157219</td>\n",
+       "      <td>0.149159</td>\n",
+       "      <td>0.155383</td>\n",
+       "      <td>0.148974</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.148979</td>\n",
+       "      <td>0.149209</td>\n",
+       "      <td>0.149046</td>\n",
+       "      <td>0.149239</td>\n",
+       "      <td>0.149137</td>\n",
+       "      <td>0.149173</td>\n",
+       "      <td>0.148949</td>\n",
+       "      <td>0.149081</td>\n",
+       "      <td>0.148663</td>\n",
+       "      <td>0.149112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 251 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               classifier        T0        T1        T2  \\\n",
+       "accuracy                         0.804398  0.781744  0.791469  0.794784   \n",
+       "f1                               0.584897  0.554277  0.511772  0.570437   \n",
+       "statistical_parity_difference   -0.191483 -0.193717 -0.158251 -0.188583   \n",
+       "equal_opportunity_difference    -0.111895 -0.088546 -0.105207 -0.097876   \n",
+       "average_abs_odds_difference      0.111166  0.107385  0.099070  0.105711   \n",
+       "disparate_impact                -1.131371 -1.026553 -1.187010 -1.062033   \n",
+       "theil_index                      0.149112  0.156897  0.177942  0.153081   \n",
+       "\n",
+       "                                     T3        T4        T5        T6  \\\n",
+       "accuracy                       0.797436  0.796000  0.801746  0.801746   \n",
+       "f1                             0.548188  0.573869  0.566248  0.582985   \n",
+       "statistical_parity_difference -0.170619 -0.187544 -0.174016 -0.186649   \n",
+       "equal_opportunity_difference  -0.085252 -0.062743 -0.068929 -0.071837   \n",
+       "average_abs_odds_difference    0.093289  0.090209  0.085960  0.092036   \n",
+       "disparate_impact              -1.122103 -1.047442 -1.084968 -1.061997   \n",
+       "theil_index                    0.164190  0.151774  0.157219  0.149159   \n",
+       "\n",
+       "                                     T7        T8  ...      T240      T241  \\\n",
+       "accuracy                       0.802409  0.803846  ...  0.804177  0.804398   \n",
+       "f1                             0.570605  0.584795  ...  0.585012  0.584702   \n",
+       "statistical_parity_difference -0.174081 -0.182725  ... -0.190958 -0.189646   \n",
+       "equal_opportunity_difference  -0.060978 -0.066391  ... -0.106214 -0.104650   \n",
+       "average_abs_odds_difference    0.082052  0.087186  ...  0.108492  0.107112   \n",
+       "disparate_impact              -1.062977 -1.050385  ... -1.122019 -1.117376   \n",
+       "theil_index                    0.155383  0.148974  ...  0.148979  0.149209   \n",
+       "\n",
+       "                                   T242      T243      T244      T245  \\\n",
+       "accuracy                       0.804288  0.804288  0.803956  0.804177   \n",
+       "f1                             0.584954  0.584565  0.584543  0.584623   \n",
+       "statistical_parity_difference -0.190466 -0.190313 -0.191461 -0.190805   \n",
+       "equal_opportunity_difference  -0.105692 -0.104650 -0.105692 -0.105171   \n",
+       "average_abs_odds_difference    0.107992  0.107544  0.108663  0.108043   \n",
+       "disparate_impact              -1.120281 -1.122222 -1.126281 -1.123964   \n",
+       "theil_index                    0.149046  0.149239  0.149137  0.149173   \n",
+       "\n",
+       "                                   T246      T247      T248      T249  \n",
+       "accuracy                       0.804288  0.804509  0.804619  0.804398  \n",
+       "f1                             0.585149  0.585034  0.585948  0.584897  \n",
+       "statistical_parity_difference -0.191297 -0.190313 -0.191461 -0.191483  \n",
+       "equal_opportunity_difference  -0.106214 -0.105171 -0.107256 -0.111895  \n",
+       "average_abs_odds_difference    0.108684  0.107685  0.109086  0.111166  \n",
+       "disparate_impact              -1.125703 -1.122222 -1.126281 -1.131371  \n",
+       "theil_index                    0.148949  0.149081  0.148663  0.149112  \n",
+       "\n",
+       "[7 rows x 251 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "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
+}