diff --git "a/AdultNoteBook/Kernels/ExtraTrees/.ipynb_checkpoints/1-income-prediction-84-369-accuracy-checkpoint.ipynb" "b/AdultNoteBook/Kernels/ExtraTrees/.ipynb_checkpoints/1-income-prediction-84-369-accuracy-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/AdultNoteBook/Kernels/ExtraTrees/.ipynb_checkpoints/1-income-prediction-84-369-accuracy-checkpoint.ipynb"
@@ -0,0 +1,3157 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "26ca1808-47be-4291-bae6-aee31907dfd0",
+    "_execution_state": "idle",
+    "_uuid": "8b5645b4fc767f691c88c0beeb918337c49b6433"
+   },
+   "source": [
+    "#Income Prediction Problem\n",
+    "In this Notebook, I am working through the Income Prediction problem associated with the Adult Income Census dataset. The goal is to accurately predict whether or not someone is making more or less than $50,000 a year. While working through this problem, I am following a framework I use to attack all my machine learning problems. It includes the following steps:\n",
+    "\n",
+    "1. Load Libraries\n",
+    "2. Load Data\n",
+    "3. Analyze Data\n",
+    "4. Feature Engineering\n",
+    "5. Modeling\n",
+    "6. Algorithm Tuning\n",
+    "7. Finalizing the Model\n",
+    "\n",
+    "I hope you enjoy this notebook and find it useful. Please keep in mind this is my first Notebook on here so don't judge it too harshly!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "2249cd4d-d941-4015-8103-9dcdec8bc587",
+    "_execution_state": "idle",
+    "_uuid": "b36102e03c9ec96cd20c1f6d40a1f8591d45c0c6"
+   },
+   "source": [
+    "##1. Load Libaraies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "bfca2f2a-d078-4f0a-ad8a-3d3571a08f76",
+    "_execution_state": "idle",
+    "_uuid": "e5e4b5ee88062058ceff62de7993eac563c1582b"
+   },
+   "source": [
+    "First, we need to load all of our libraries we will use for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "_cell_guid": "9201e3be-d4ec-4e38-9e7b-eb7bf54d7c25",
+    "_execution_state": "idle",
+    "_uuid": "de4cbbff6d2b51eb47a5a4719da64cd27804989d"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve, train_test_split, KFold\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "sns.set(style='white', context='notebook', palette='deep')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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": {
+    "_cell_guid": "bf1d51ca-c701-44dd-a305-dbbfd4bb0cec",
+    "_execution_state": "idle",
+    "_uuid": "161e57d369bf62b7ad27912cc4ae0116ac5de0b6"
+   },
+   "source": [
+    "##2. Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "960b99a1-4408-4c1a-b86b-788c17e3b18b",
+    "_execution_state": "idle",
+    "_uuid": "0ecc4df26f5b56f9bfc4ed1841a73c8aafa00837"
+   },
+   "source": [
+    "Next, we load our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "_cell_guid": "946806b5-f230-4216-b42e-a8358e51b605",
+    "_execution_state": "idle",
+    "_uuid": "c4141c604b71fa95c187aa09fe7bed39106fcb1d"
+   },
+   "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": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv(\"../../Data/adult.csv\")\n",
+    "\n",
+    "# Check for Null Data\n",
+    "dataset.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "_cell_guid": "25324eeb-2942-45fe-afae-0532421929cb",
+    "_execution_state": "idle",
+    "_uuid": "771a425b9b2a13c2266d851158fbc9e53211e118"
+   },
+   "outputs": [],
+   "source": [
+    "# Replace All Null Data in NaN\n",
+    "dataset = dataset.fillna(np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "_cell_guid": "86a2ec1f-d7e8-4f29-a949-68240fe69d87",
+    "_execution_state": "idle",
+    "_uuid": "68a3687580bd65fb09a100d49cecfe1774df79dc"
+   },
+   "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": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get data types\n",
+    "dataset.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "_cell_guid": "1cbf148a-b814-40a3-9154-70c4511bdcd2",
+    "_execution_state": "idle",
+    "_uuid": "5fd35d640fdd346662ac08f7ab16ab996dfeb864"
+   },
+   "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",
+       "  </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",
+       "\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",
+       "\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  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Peek at data\n",
+    "dataset.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "_cell_guid": "c183daab-b3b2-4cab-91a0-05f0b7a98dce",
+    "_execution_state": "idle",
+    "_uuid": "0b0c1b473300ee13c423318626c371835d032e12"
+   },
+   "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>0</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>0</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>0</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>0</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",
+       "\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",
+       "\n",
+       "   capital.loss  hours.per.week native.country  income  \n",
+       "0          4356              40  United-States       0  \n",
+       "1          4356              18  United-States       0  \n",
+       "2          4356              40  United-States       0  \n",
+       "3          3900              40  United-States       0  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Reformat Column We Are Predicting\n",
+    "dataset['income']=dataset['income'].map({'<=50K': 0, '>50K': 1, '<=50K.': 0, '>50K.': 1})\n",
+    "dataset.head(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "7ad8dd1d-470b-4e1e-b7b5-44769b775131",
+    "_execution_state": "idle",
+    "_uuid": "6cb8261a58c38c0492512efdbd1621d910965d52"
+   },
+   "source": [
+    "##3. Analyze Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "_cell_guid": "1c4744ab-7e9a-4993-ae0e-3c725912c638",
+    "_execution_state": "idle",
+    "_uuid": "31c7a7019b800cdea2eae4d2904b3ad428219da2"
+   },
+   "outputs": [],
+   "source": [
+    "# Identify Numeric features\n",
+    "numeric_features = ['age','fnlwgt','education.num','capital.gain','capital.loss','hours.per.week','income']\n",
+    "\n",
+    "# Identify Categorical features\n",
+    "cat_features = ['workclass','education','marital.status', 'occupation', 'relationship', 'race', 'sex', 'native']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "0ade92f2-fcb2-487d-a581-34a89f85e3c5",
+    "_execution_state": "idle",
+    "_uuid": "1d0cdf67c8b35f2389931629a5f105823a42c804"
+   },
+   "source": [
+    "###3.1. Numeric Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "_cell_guid": "7004fe8d-fefd-4ade-805a-1dddf9c88928",
+    "_execution_state": "idle",
+    "_uuid": "bd1dbe810e1435bf36a9e262a115382c6c2761f4"
+   },
+   "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"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'seaborn' has no attribute 'plt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-50-b46d4f116953>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Count of >50K & <=50K\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'income'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Count\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'plt'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count of >50K & <=50K\n",
+    "sns.countplot(dataset['income'],label=\"Count\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "_cell_guid": "dc5bcab4-92fc-461c-9d7c-1a9580d23846",
+    "_execution_state": "idle",
+    "_uuid": "c92ee829ae5ca6724f3778a45f4e2a3df5f88c2a"
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'seaborn' has no attribute 'plt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-51-cfed1456d88b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Correlation matrix between numerical values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mannot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\".2f\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"coolwarm\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'plt'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MatasSVBQkNFr9+/f5+jRo+zevRuAsWPH4uPjA0C7du1o167df56nIAhCURWHIdum+n9fjAri7e3NlStX6NatGzKZjJYtW9K2relnGIIgCMVBSRrAIIpRAdRqNd98843UaQiCIJilOAzZNpUoRoIgCKWUXCmKkSAIgiAx0U0nCIIgSE500wmCIAiSE6PpBEEQBMmJbjpBEARBcqKbThAEQZCcGE0nCIIgSE60jIRCFcd54CbvHS11CoVaaL9e6hQKlOb63946xFQ5lrodrIXptJaZcfxF2PkgRuoUCjSqw0tmr0NcMxIEQRAkJ1pGgiAIgvSedpvjYkYUI0EQhFJKriy+t+54kihGgiAIpZS4ZiQIgiBIriRdMyo5mQqCIAhFIpPLTH4URVBQEK+//jodO3Zk/fr8I16vXbtG37596dGjB++++y5JSUnPXKcoRoIgCKXUiyhG0dHRLFy4kA0bNhAYGMjmzZu5e/euUczs2bMZP348O3fupEqVKqxateqZ6xXddIIgCKVVEbrpkpKSCmzBODo64ujoaHh+8uRJmjdvjrOzMwCdOnVi7969+Pv7G2J0Oh2pqakApKen4+T07N/liWIkCIJQSskVpo+mW7NmDUuWLMn3ur+/PwEBAYbnMTExuLu7G557eHhw+fJlo7+ZNGkSI0eO5KuvvsLGxoYtW7Y88/1FMRIEQSilitL9NmLECHr37p3v9cdbRQB6vT7/+zz2e6aMjAymTp3KmjVrqF+/PqtXr+bTTz9lxYqnzz4jilEx1KJJGd4dXgW1Ss69kFTmfH+LtPT806kUFieXw8R3q9Owbm7T+PQFDT/8HGyx/OqvmkPK1TsEL/w53zKPLq2pOftD5Go1yVducXn0FHKSU0Eup/aCybh3bIlMqSB44c+ErthksZx8X3bi7YHeqJRygkPT+GZFCGnpuueK+2KiD4/is1nyS6hZOTWuY8vQHm6olDIePMxkyYYY0jN0JsXIZTCyjxsNX7JDoYDAQwnsO5FoVj5PalLXnuG93VEpZYQ8zOT7tZH58jMlzs1FyYJPKzN+5n2SUs2f9qdpPXtG9C2b+37hGXz3y8MC8yosTq2SMXaIFzUq2yCTwa376SxdH0FWdv6DaFEU1+31VEXopnuyO64wnp6enD9/3vA8JiYGDw8Pw/Pbt29jZWVF/fr1ARgwYACLFi16dqomZ1rCTJ48mU6dOrFr164Cl9esWdOs9YeFhTFlyhSz1lEQZ0cVUybUZNqc6wwee46IqHTGvlmlSHGd2npS0duGEQHneXP8BRrWdaLtq25m52Zfqyq++9fg1a9LgcvVbi7U/2kOF/oHcLRuZ9Luh1Hrq48AqDR6IHbVKnGsYTdOtOhHlYARODWtZ3ZOAE4OSj56tzIzFt5j5IdXiYzJ5J1B5Z8rrn/3stSrZW92To72CgKGejLvp0j8Zz4g6lE2w3q4mhzzWksnynmomfDVAz6eF0a3Ns5Ur2Rldl6Pv/eEEeWYszycsV8EExWXxZu9PYoc17a5E3M/qoSri8pieb0/sjxf/RjKu9PuEBWbxci+nkWKG9DVHYUc/GfcxX/6XaxUMvq/7p5vHUXNqzhur2d5EQMYXnnlFU6dOoVGoyE9PZ39+/fj5+dnWF6pUiWioqIIDs49AT506BD16j37u15qi9H27dsJCgqiW7duL2T9ERERhIWFWXy9TV924cadZMIj0wHYvieCjq3zfxmfFieXy7CxVqBSyVGrZKiUcrKyzDsrBKg0dgjha7YRsXVPgcvdOrYk8fwV0u4+AODB8o14DeoOgGfPDoSt2YZeqyUnIYmILbvxHtzD7JwAGtd35HZwKg+jMgEIOhBL+1fLFDmuQW0HmjZwZNfBWLNzaljLljsPMomMzQZg7/FE/Jo6mBzj28Cew6eT0OkgNV3Hib+T8Wv67LNWU71c2447DzKIjMl97z1HE2jtm3/9T4sr46SkeUN7Ziyx3PegUR177oSkExGTBcDuIxra+DoXKe7q7TQ27Y5FrwedHu6FZeDuat7Bv7hur2eRyeQmP0zl6enJxIkTGT58OL169aJbt27Ur1+fUaNGceXKFZycnJgzZw7vv/8+3bt35/fff+err7565npLZTfdmDFj0Ov1vPLKKzg6OtK4cWNu3LiBq6srixYtMowC0Wg09OjRgxMnTgDQqlUrJk+ezOuvv86KFSuQyWQMHDiQTz75hNDQUCpUqEBUVBRLlixh1qxZhIeHM2PGDL744guL5e7pbkVMXKbheWxcJvZ2SmxtFEZddU+L23MoinavurPjl+Yo5DLO/hPPX+cemZ3btQkzAXBt17zA5Tbly5IeHmV4nhEehcrJAaWDHTbly5ERHpm37GEUjvXMa53+y8NVTcyjLMPzWE0WdrZKbG3kRl1wT4uzsVYwbkQFJs25Q7f25p1FQ25XzKOEvBm0HyXkYGejwMZabuiyeVqMm7OSuPjHlsXnUNnLci0jdxcVcZpsw/O4+Ox8+T0rTpOYw5xlDy2WE4B7GRWxT76fbQF5PSXu4vUUo7ieHVxZvDbCvLyK6fZ6FtkLmg6oe/fudO/e3ei1lStXGv7dunVrWrduXaR1lsqW0bJlywDYsWMHERERjBw5kl27duHo6EhQUJAhrkyZMnh5eXH79m3u3buHVqvl7NmzABw7doy2bdvyww8/UKVKFXbv3s24ceO4desWANOmTaNu3boWLURgfCHwcTqd3uS4kYMqE5+UTfdhp+g98jSO9ioG9srfbWVxhfRP67U6KKAbQK/N39/+PAqbC1KnMy1OhoypAVX5cW0YmoTsgoMslpPepJiCNuWT+4A5CjsRzrefmRhnKZbY//9VrZI18z6twq7DGs5dTjYvr2K6vZ7lRf3o9UUolS2jx7m6ulK7dm0AqlevTmKi8UXg1q1bc+rUKZRKJcOHD2f37t0kJycTGxtLtWrV+Ouvv1iwYAEA9erVM/taU0HeHlKZls1yrxXY2Sq4F5JqWObmakVScjYZmcZH1ujYDGrXcCgwrnULNxYuv0tOjp6cHC17DkfR5lV3Nu0It3juj8sIi8S5WQPDc2tvT7I0CWjT0skIi8SqbF6Lw9rLk/SHUQWtxiQj+nnRorEzAHY2Cu6HpRmWuZVRk5SSk2+bxTzK4qVqdvniKpa3pqyHFWOGVgCgjLMKuRzUKhnfrnzwXPnFxedQo7K14bmrk5LkVC2Zj3WXPi0mVpODi2PeWW0ZZyVxCebdq2hIdzeaNcjdZ2yt5YQ8zGtZuzrnzw8gVpNNjco2z4wzx9CeHvj+m5eNgpDwjMfeT0Vyak4BeWVRs4pNoXF+TZ14b2g5lq6P5OjZ5xv4UVy3V5EUoftNaiUn0+dkZZXXtSGTyfINS/y3GJ05c4aOHTsil8sJCgqiVatWACgUigKHMlrSqvUhjJxwgZETLjD6o4vUqelI+XK5O3SvLl4cP5O/i+3sxfhC427fS6ZdS/f/5S+jZTNXrt989nQc5oo9cAIX3wbYVqsEQMXRA4kOOgRAdNAhKrzZF5lCgdLJAa/+XYkOPPjc77VmawRjJl9nzOTrBHx+g5eq2+NdNvez7t7BnZPnE/L9zYXLSQXG3biTymD/y4b17ToYy5FT8c9diAD+uZFGjcrWlHPPvVbRqZUTZ6+kmhxz9koK7Vs4IpeDrY2cVo0dOHvZ+O+Lan1QHBNm3WfCrPt89HUINavaUM4j9727+Llw5lL+1sPF66kmxZnj18AYAr68R8CX9/jgq3vU9LHFy0MNwOttynD6n/zv9/e1lELjXm3syLuDyjHt25DnLkRQfLdXUYiWUQlSp04d7t+/j5WVFT4+Pvj6+rJ06VLmzJkD5I4cCQoKolatWty6dYs7d+4gk8lQKBTk5Fj+rpoJidl8tegWsybXRqmU8TAqg1nf3gSgZjV7JgXUZOSEC0+N+/6ne0x8txrrlzZFp9Nz/lI8v/7+Yi6aOjWuS73lszjRpBdZsRouvTOZxpu/R65SkRocyqWRnwLwYNlGbKtWpNWFQORqFaErN6M5fs4iOSQk5TB/WQifv++DUikjMjqTr3+8D0CNqrZ8MKoyYyZff2qcpSWmaFn8azQfv10OlVJGVFw2i9ZG4VPRinGDPflgbmihMZA7mKGsm4qFkyuiVMjY/1ci1+6mWy6/ZC2L1kQweXR5lEoZUbFZfLs697pKtUrWBAwrx4RZ958a9yIkJmv5bnU4k8dWQKWUERmTxTc/PzTkNWGENwFf3ntq3Ig+nshkMGGEt2G91++msXRDZIHvaWpexXF7PVMJmihVpn/Rp/0SqVmzJocOHWL48OEcPnwYgMWLFwMQEBBAzZo1Ddd/pkyZQnp6OgsXLuTw4cN8+OGHnDlzBrVaTUpKCpMnTyYkJISKFSty9epVtm3bhlwuZ9iwYbz00kvMnz/f5Lxadj9q+f+smYr1bcf7Fc/bjjuI244XSXG+7XhRZin4LwUtN/+248mLPjQ51mHCN2a/nzlKbTGylMDAQMqXL0/jxo2JiIhg6NChHDx4EPlznnGIYlQ0ohgVjShGRVeai1HKD5+YHGs/bp7Z72eO//fddM9StWpVvvjiC3Q6HXK5nC+//PK5C5EgCMJ/qgQNYBDF6Bnq1avHtm3bpE5DEASh6IrBwARTiWIkCIJQShVlZgWpiWIkCIJQWomWkSAIgiA1WQm6vi2KkSAIQmlVTEcKFkQUI0EQhNKqsAkQiyFRjARBEEop0U0nCIIgSE+MphMEQRAkJ0bTCYWpVLea1Cnks9C+eE65AzBx6xCpUyjQsR/+kTqFAiUlZj47SAIennbPDpJIywaWua9WcSQTAxgEQRAEyYluOkEQBEFyYjSdIAiCIDkxmk4QBEGQnOimEwRBECQnRtMJgiAIkpOL0XSCIAiC1MQ1I0EQBEFyYjSdIAiCIDkxgEEQBEGQnOimE8zRsKYVAzo5oVRCWFQOK3+PJz1TX+SY94eUIT5Zy5qdiRbLzfdlJ94e6I1KKSc4NI1vVoSQlp5/OhVT4r6Y6MOj+GyW/BJqkdzqr5pDytU7BC/8Od8yjy6tqTn7Q+RqNclXbnF59BRyklNBLqf2gsm4d2yJTKkgeOHPhK7YZJF8alWQ07mZCqUCIjV6th7NIjO74Ng3WquIjtdz7HIOAEoF9Gqpory7HDkQGqtjx4lscrQWSY26VZX09LNBpYDwWC2/7k0jI8u0GJkMBnawoXr53MPH1fvZbDuSYZG8qnvL6NBIgVIO0fF6Ak9pC91mvV5REJOg5+T1vP3qk/5KktLyYv66puXKfX0Bf100V/8+xs4Ni8jJzsK7Ug0Gj5mBja29UczRvRs5vn8LMhm4eVZg8Ltf4ODkahSzcsFEnFzc6f/2FLNzMkkJ6qazaNlcvHgxixcvttj6kpOTee+99wCIjo5m1KhRFlt3ceVgJ2d0Pxe+W/+Ij7+NIUaTw4DOjkWO6eZnT83Kaovm5uSg5KN3KzNj4T1GfniVyJhM3hlU/rni+ncvS71a9vn+9nnY16qK7/41ePXrUuBytZsL9X+aw4X+ARyt25m0+2HU+uojACqNHohdtUoca9iNEy36USVgBE5N65mdk501vNFGzboDWSzYkokmSUeXZqp8cR7OMkZ1VVPfx3jUU7uXlchlsGhrJgt/z0SlgLYNLXPuaG8jY3hnW1bsSGX6qmTiEnT08rMxOca3thpPFzkzf0lm1ppkqpdX0qhG/v9bUdla5RaYzUdyWByYQ3yKng6N8h+i3JxgREcFdSobH2hdHSE9C5btyjE8LFGIkpM0/PrjZ7zz4bd8vigIV4/y7NzwnVFMaPB1DgWt4cNZa5n6zXbcy1Zk1+YfjGIOBP7MvRt/m51PkcgVpj8kVqzbcImJidy8eRMAT09PVq5cKXFGL1696lYEh2cT/Sj3FPjg6VRebWhbpJjaVdXUr2HNobOpFs2tcX1Hbgen8jAqdzLOoAOxtH+1TJHjGtR2oGkDR3YdjLVIXpXGDiF8zTYitu4pcLlbx5Yknr9C2t0HADxYvhGvQd0B8OzZgbA129BrteQkJBGxZTfeg3uYnVP18grCYnU8Sso9GJ6+ruXl6vm/8C3qKDl/W8vle8ZNnvuROg5fzEEP6PUQ8UiPi4NlznJfqqwkJEpLbEJui+LYP1k0q602OUYuB7VKhlIBKgUoFTKyteYf9H28ZEQ80qNJzn1+7paO+lXyH6Ka1ZRz8Z6OayHG71nBXYZeB2++pmBsdyWt68st0jC4eekUlXzq4lGuEgCtXuvPueN/oNfnvX/FqrX5YlEQNrYOZGdlkqiJwc7eybD89tWz3PjnL1p2fMP8hIpCLjf9IbEiZbBixQp69+5Njx49mDdvHnq9np9++onXXnuNAQMGcPnyZUNszZo1Df/etm0bkyZNAuDkyZP06NGD7t278+6775KSkkJKSgrjx49nwIABtG3blo8//hi9Xs+sWbOIiYlh3LhxhIeH065dOwDi4uJ499136d69O7179+bYsWNAbsts2rRpDBs2jHbt2rF06dJ8/4czZ87w1ltv8d5779GpUyfGjx9PVlaW0fr/Xde/rbxXX32VadOm0blzZ4YNG8aePXsYPHgw7dq14+zZs0XZhM/k6qRAk5h3YNIkabG1lmNjJTMpxtlBzrBuzvy4WYPOwpMRe7iqiXmU15cTq8nCzlaJrY3c5DhXFxXjRlRgzpL7Fsvv2oSZPFwfWOhym/JlSQ+PMjzPCI9C5eSA0sEOm/LlyAiPzFv2MAob77Jm5+RsJyMxJe9glZiqx1otw+qJBkTgX9lcvJO/7+3OQx1xibl/72wvo2VdJZeDLdNH5+IgJz45b+MnJOuwsZJhrTYt5tTVLNIy9cwd68jc95yITdBy5V6O2Xk52clITM3bZklpFLjN/jir43Jw/uKnkMO9SB3rDmpZvTeHal4yfGuZf5CNfxSFs2vePuHs6klGegoZ6cYnewqliktnDzNtbEfu3vib5m17AZCgiWHrL18zYvzc//xmd3qZzOSH1EzeMseOHePq1ats3bqVHTt2EB0dzdKlS/n999/Zvn07q1evJioq6qnryMrK4qOPPuLrr78mKCiImjVrsn37do4cOcJLL73E5s2b2bdvH//88w/Xrl1j2rRpeHh48MMPxs3dmTNn0rx5c4KCgvj++++ZMmUKcXFxANy6dYtVq1bx22+/sWLFCpKSkvLlcfHiRT7//HP27NlDREQEJ06ceGrecXFxtGnThr179wJw8OBBNmzYQEBAAGvWrDF1E5qksH3i8QP30/abgEFlWLcrgYRky0+Lb0puT4uTIWNqQFV+XBuGJqGQCwEvQiEHAL1WV+Av1PVa87ddoduqiA0IbzcZY3uoOXkth5uhlvlMC/tR/uO5PS2m6yvWpKTp+eSHJCYvTcTWWkb7JlZm51XYbm3qNrtwR8+eczq0OsjIhpPXdbxUwfyDrL6QsyZ5AftVg2bt+HrVMV5/Yww/zB5DdnYWqxd9Qt83P8HJxd3sXIpMJjf9UQRBQUG8/vrrdOzYkfXr89+CJjg4mGHDhtGjRw/efvttEhOffd3a5E7oU6dOcfnyZfr06QNARkYGBw4cYPDgwdjZ5d6rpHPnzuiecrp769YtPD09eemllwD44IMPDMsuX77ML7/8QnBwMAkJCaSlpeHs7Fzgek6fPs2sWbMAqFChAg0aNODSpUsA+Pr6olarcXV1xdnZmeTkZBwdja+nVK9enbJlc890fHx8TNpQfn5+AHh7e9O4cWMAvLy8Cix25niUoKVahbxT1DKOClLSdGRm658Z4+2pwt1FwdCuud0DTg4K5DJQKWX8tC3hufIZ0c+LFo2dAbCzUXA/LO/qsFsZNUkpOWRkGn/mMY+yeKmaXb64iuWtKethxZihFXLzdlYZuny+XfngufIzRUZYJM7NGhieW3t7kqVJQJuWTkZYJFZl8w4S1l6epD98+kmVKRJS9FTwyPuCO9rJSMvQk12EBkQDHwW9XlUR+Fc2/9yz0MgFQJOso3K5vK++s4OM1HQdWdmmxbxcXcXmQ+lodaDNgtNXs2lUU8Wh8+bdSykxVU9597xt5mALaZmmb7P6VWVEa/REJ+Q+lwEW6D3Exa0cIXev5OWpicHWzhEr67yu8dioUJIS4vCp1QiAFu16s2nlLELvXeNRzEO2rVkAQFJCHHqdjuzsTIaMmWF+cs/yAoZ2R0dHs3DhQrZt24ZarWbgwIH4+vpSrVruvdr0ej1jx45l6tSp+Pn5sWDBAlasWMHHH3/81PWaXIy0Wi0jRoxg5MiRACQlJbF27Vqjg7FSqSQrK697Rq/XI5PJyMnJ3ZtUKuP2dnJyMqmpqRw4cIB9+/bRv39/XnnlFW7fvm3UH/ukJ5fp9Xq02twvq5VV3hmaTCYrcD0FxTwZm5OTg1KZt3nU6ryDv+IF3rDqyp1MhrzuhKerguhHWtr72nHherpJMXdDsxj/dbQhrk97Bxzs5GaNpluzNYI1WyMAcHZUsnJeHbzLWvEwKpPuHdw5eT4h399cuJzEmKEV8sXduJPKYP+8rtzhfb1wdFBabDRdYWIPnOCleZ9iW60SaXcfUHH0QKKDDgEQHXSICm/2JWbXnyjsbfHq35Ur474w+z1vh2vp2lyFq6OMR0l6mr+k4PoD0wtKvSpyeryi4qc/MnkYZ4Ej6mNuhOTQt40N7s5yYhN0tGpgxaW72SbHhMZoaVxLxe2wHORyqF9Nyf0I87vp7kXq6dRERhkH0CRD0xpyboWZ/n/3dJZRu6KczUe1KOTQrJacK/fNb02+1KAF29ctICbyAR7lKnH8wG/Ua9rWKCYxPpZfFn3KpHm/Ye/owrnju/GqWA2fWi8za+kBQ9zuLT+Smpzwn42m07+AgQknT56kefPmhsZCp06d2Lt3L/7+/gBcu3YNW1tbwwn8mDFjTDppN7kYNW/enO+//57+/ftjZWXFuHHjaN++PTt37iQgIAC1Ws2BAwdo3bo1AC4uLty5c4fq1atz+PBhnJ2dqVKlChqNhrt371KtWjV++uknILfFNGDAALp3786dO3e4efMmOp0OpVJpKGRP5rJ161ZGjhxJWFgYf//9N9OnT+fWrVum/nfycXR0JDExEY1Gg729PcePH6dt27bP/kMLS0rVsfz3eCYMcUWpgBiNlqVbNFTxVjGqjzNTFscWGvOiJSTlMH9ZCJ+/74NSKSMyOpOvf7wPQI2qtnwwqjJjJl9/atx/xalxXeotn8WJJr3IitVw6Z3JNN78PXKVitTgUC6N/BSAB8s2Ylu1Iq0uBCJXqwhduRnN8XNmv39qBvx2NIuhHdUo5fAoSc/mI1l4u8no56dm0bantyI6/2/kXT+/vJOgkGgdgX+Z372ZnKZn7Z40Rve0Q6GAuAQdv/yRRkVPBUM72/LVmuRCYwC2Hk5nQHsbvnjLAZ0ebj3IYd9Z8+8wm5oBO05qGdBaiUIOmhQ9209o8XKV0aOFgmW7nl7wjlzS8bqvgve65/79tQc6Ltwxv5A7OLkydOxMVn37ITk52bh5VmC4/2we3LvGhmXTmTz/N6q91JhOfUaxaMZbyOVKnMq4M+rj78x+b7MV4VpQUlJSgUXD0dHRqHcpJiYGd/e83gQPDw+j8QKhoaG4ubnx6aefcv36dWrUqMFnn332zPc3uRi1a9eOmzdv0r9/f7RaLa1atWLEiBGoVCr69euHo6MjXl5ehvgPP/yQMWPG4ObmRuPGjYmPj8fKyor58+fzySefkJ2dTcWKFZk3bx6XL19m+vTp/Pzzz9jZ2fHyyy8THh5OkyZN8PLyYtiwYcyZM8ew7qlTp/L555+zbds2AGbNmoWHh0ehuUdHRzN69GgCAwu/yO3g4MDbb79Nv379KFu2LPXqmT+893ldupXJpVsxRq/df5jNlMWxT4150rZDyRbP7ew/iZz9J39L63ZwGmMmX39m3OPW/h5h0dwuvz3Z8O/EC1c50aSX4Xns3mPE7j2W72/0Wi3XP/zKonn861aYjlthxgfph5n6AgvRb0eNi8z8zS/29uHX7udw7b7x/hGaoeWrNclPjQFIzdDz8+60fK9bwp2Heu48NC466Y/0BRaiHSeNW5rZWgg8abnuzMfVadSKOo1aGb1mZ+/E5Pm/GZ63em0ArV4b8NT1dO3/3gvJr1BFGDCxZs0alixZku91f39/AgICDM8L6m2SPVb0cnJyOHv2LL/++iv16tXju+++Y+7cucydO/ep7y/TP60/TLC4IZMfSp1CPtEhkc8OksjErUOkTqFAx374R+oUCpSU+GKL2PPy8LR7dpBEWjaw/GAfS+jYwPxBIaknt5kcq63bwaSW0fbt2zl//jyzZ88G4IcffkCv1xu66U6dOsWcOXPYuXMnAHfv3mX8+PH88ccfT31/6QeXC4IgCC9GEUbTOTo6Ur58+XyPJweAvfLKK5w6dQqNRkN6ejr79+83XB8CePnll9FoNIbfiB4+fJg6deo8M1UxHZAgCEIppX8Bo+k8PT2ZOHEiw4cPJzs7m379+lG/fn1GjRrF+PHjqVevHj/88APTpk0jPT2dsmXLMm/evGeuVxQjQRCEUupFjKYD6N69O927dzd67fEZcho0aMDWrVuLtE5RjARBEEqrYjCzgqlEMRIEQSitxP2MBEEQBKkVhznnTCWKkSAIQmklWkaCIAiC1PSFTj9b/IhiJAiCUErp5SXnEF9yMhUEQRCKRFwzEgrl4Gz77KD/WJqr07ODJFJcp93xG9dQ6hQK9P3gzVKnUCC9rpLUKRTqnqeL1CkUqGODZ8c8y4v40euLIoqRIAhCaSVaRoIgCILURMtIEARBkJxO9uJuBGppohgJgiCUVqKbThAEQZCavgTdJUgUI0EQhFJKDO0WBEEQJCcGMAiCIAiSE9MBCYIgCJLTvaCb670IohgJgiCUUqJlJJilblUlvVrboFTIeBirZd2eVDKyTIuRyWBgR1tqVMj9aK8GZ/P7n+kWyatxHVuG9nBDpZTx4GEmSzbEkJ6hMylGLoORfdxo+JIdCgUEHkpg34lEi+RVq4Kczs1UKBUQqdGz9WgWmdkFx77RWkV0vJ5jl3MAUCqgV0sV5d3lyIHQWB07TmSTo7VIagDUXzWHlKt3CF74c75lHl1aU3P2h8jVapKv3OLy6CnkJKeCXE7tBZNx79gSmVJB8MKfCV2xyXJJAc0aODLyjXKolDLuh2WwcFUoaU98nk+Ls7WR88HbFalQzgqZTMbBExq2/BFjVk7Fdd8PvnaEv4K+QZuThZtXTToO+gorG3ujmBvnAjl/aBUymQylyoY2/aZStmI99Dodx4MWcP/aUWQyOS7ulWg/4EtsHcpYJLenKUnXjEpOpsDGjRvZuHEjAJMnT+bhw4dPjR82bBhnzpwx+31HjRpFdHS02esxhb2NjOGv27FiRyrTf0oiLkFH79Y2Jsc0r6PGs4ycL39OYubqJKpXUNKopsrsvBztFQQM9WTeT5H4z3xA1KNshvVwNTnmtZZOlPNQM+GrB3w8L4xubZypXsnK7LzsrOGNNmrWHchiwZZMNEk6ujTL///1cJYxqqua+j7G3RbtXlYil8GirZks/D0TlQLaNrTMOZp9rar47l+DV78uBS5Xu7lQ/6c5XOgfwNG6nUm7H0atrz4CoNLogdhVq8Sxht040aIfVQJG4NS0nkXyAnByUPDhOxWYufg+70y6SVRsJm/19ypS3Ig+5YjTZPPu1FsETL9N13ZuvOTz/HMvFtd9Py1Zw/71k+n21mLenLYPJ9cKnAhaYBSjiQ7mWOB8eo/9iaGfBuLbaSy7VgUAcPX078SEXWPIx9sZPjkIJ/eKHNsx1+y8TKFHZvJDaiWqGA0aNIhBgwYBcObMGfR6/X/yvitXrsTT0/M/ea/aVVQ8iNISE597hnrsYibN6liZHCOTg5VKhlIBKkXumX92jvl5Naxly50HmUTG5jY59h5PxK+pg8kxvg3sOXw6CZ0OUtN1nPg7Gb+mjmbnVb28grBYHY+ScveF09e1vFw9fz95izpKzt/WcvmecZPnfqSOwxdz0AN6PUQ80uPiYJkvZqWxQwhfs42IrXsKXO7WsSWJ56+QdvcBAA+Wb8RrUHcAPHt2IGzNNvRaLTkJSURs2Y334B4WyQugUV1HbgWnERGd2+zYdfgR7VrknzD0aXFL1z9kxabcE0JXZyUqlYzU9OdvUhbXff/BzROUrVgPF4/KANRvOYib54OMjj8KpZqOg2Zh7+QBgGfFuqQmxaHNycK1XDVa9fwEpUqdu6xCXZI0EeYnZgK9TG7yQ2r/WTedXq9nwYIFHDx4EIVCwYABA3jppZdYuHAhGRkZJCYm8vHHH9OlSxcmTZqETCbj9u3bpKSkMHbsWHr16sXixYsBsLKyIiYmhtGjR7N+/XpOnz7N6tWrycjIIDMzk1mzZtG0adNCc1m7di2//vorDg4OVK1alYoVKxIQEMCvv/5KYGAg6enpyGQyvvvuO3x8fGjXrh1r167l7NmzHD9+nMTERMLCwnj11VeZPn26RbeTi4OM+KS8rpL4ZB02VjKs1Ri6K54Wc+pKFo1rqpk7zgmFTMb1kGyu3Cukz6oI3FyUPErI+2Y/SsjBzkaBjbXc0FX3tBg3ZyVx8Y8ti8+hspf5LSNnOxmJKXkHhcRUPdZqGVYqjLrqAv/KfVLNy/hLd+dh3nZ0tpfRsq6S348/0S/0nK5NmAmAa7vmBS63KV+W9PAow/OM8ChUTg4oHeywKV+OjPDIvGUPo3CsV9MieQG4l1ERp8nbQLGaLOxsFdhay4266p4Vp9PBJ+9WpFUTZ/76O5HwyMznzqm47vvJCVHYu5Q1PHdwLktWRgpZGamGrjon1/I4uZYHco91R7fPoWrddiiUaryqvGz424y0RM7s+5H6rw40Oy9TFIcWj6n+s3K4d+9e/v77b4KCgvjtt9/Ytm0bP/74I7NmzWL79u3Mnj2bH3/80RAfHR3Npk2bWLNmDfPmzSM2NtawbPTo0Xh4eLBixQqcnJzYtGkTy5YtY+fOnYwaNYpVq1YVmsfNmzdZv34927ZtY8OGDTx4kHtWmpKSwsGDB1m3bh27du2iQ4cObNiwId/fX7x4ke+//56dO3fy559/cuvWLQtuJZAV8iM1nd60mG6vWpOcpuOTxYlM+jEBO2sZHZqaf9Av7LdzuscSe1qMvIA97fG/tXheRVy1t5uMsT3UnLyWw83Q/NdNXoiCNgqg1+pAnv8/ptdaLq8CVg/Ak29hSty85aG84X8VBzsFQ3qVLfgPTFBc9330BW93eQGfX3ZmGrtXTyAhNpSOg2YZLUuIDWXLoqF4VW1Eg1ZDzM/LBDqZwuSH1P6zltG5c+fo0qULarUatVpNYGAgmZmZ/Pnnn+zdu5dLly6RmppqiO/Tpw8qlYqyZcvSqFEjLly4UOB65XI5P/zwA4cPH+b+/fucPXu2wJ3kX6dOnaJt27bY2+ee0XTt2pWkpCTs7e355ptv2L17NyEhIRw/fpyXXnop39+//PLLhr+tUKECiYmWuQj/L02SjipeeTuGs4Oc1HQdWdmmxTSsoWbzwTS0OtBmwemrWbxcU8XBc89/xgoQF59DjcrWhueuTkqSU7VkZulNionV5ODimJdzGWclcQnm96EkpOip4JH3eTvayUjL0Bepe6aBj4Jer6oI/Cubf+5ZcOTCM2SEReLcLO+mNdbenmRpEtCmpZMRFolVWfe8ZV6epD+MKmg1JhveuyzNX869d5WtjZyQ8AzDMjcXFckpOWRmGR94YzTZ1PKxKzCucV0H7oeno0nIISNTx5HTCbRs8vz3xiqu+76DSzkiQy4ZnqckRmNl64TKyvj6WJImgsAVYyhT1oc3AtaiVOd9F8Jun2b3LxNp0v4dmrR/26x8iqIkzcDwn7WMlErjuhceHs7gwYO5fPkydevWZcyYMUbLFYq8HU6n0+X7+3+lpqbSt29fwsPDadq0KcOGDXtqHnK5HJ0u/5lOZGQkAwYMIDk5GT8/P3r37l3gNSkrq7wzLZlMZvHrVjdCsqnipcTDJfej8Wuo5tLdbJNjwqJzaFwrt29aLof61VTcjzD/APvPjTRqVLamnHvuBeFOrZw4eyXV5JizV1Jo38IRuTz3QNiqsQNnLxv//fO4Ha6looccV8fcL13zlxRcf2D6/7deFTk9XlHx0x+Z/2khAog9cAIX3wbYVsu98VzF0QOJDjoEQHTQISq82ReZQoHSyQGv/l2JDjxo1vut3R7Fe5/f4r3PbzHhyzvU8rHFyzN3X+nazo1TF/OfWF24klxonF8zZ4b+ryWkUsrwa+bMPzdSnju/4rrvV6rVkqgHl4iPCQHg8olN+NRrbxSTkZrAb98PpVqD1+j65kKjQhQR/DdBq/zpPPTr/7QQAej1MpMfUvvPWkZNmzZl7dq1DBo0iJycHN5++20iIyPZsGEDVlZWLF68GK02b8fZs2cPnTt3JiIigsuXLzN79mxu3LhhWK5QKNBqtYSEhCCXyw3FbNq0aUbreVKLFi0ICAhg/PjxqNVq9u/fT4sWLbhy5QqVKlXizTffJCsri2XLllGmzIsfevmk5DQ9a/9IZXQvOxQKGbHxWn7ZnUbFsgqGdbZl9i/JhcYA/HYonQEdbZn+jiM6PdwMyWbfmYxnvOuzJaZoWfxrNB+/nTvENyoum0Vro/CpaMW4wZ58MDe00BjIHcxQ1k3FwskVUSpk7P8rkWt3zR92m5oBvx3NYmhHNUo5PErSs/lIFt5uMvr5qVm07elnxZ3/N/Kun5/a8FpItM5wjcnSnBrXpd7yWZxo0ousWA2X3plM483fI1epSA0O5dLITwF4sGwjtlUr0upCIHK1itCVm9EcP2exPBKTc/jmp1A+86+CUikjMiaT+StCAahe2YaJb1Xkvc9vPTVuxaYIxo8oz/LZNdHr4eTfiezYH/u0t32q4rrv2zq48trgOez6eTw6bTZObhXpPPRrokKvcHDjNIZ+GsilExtJjo/k3uUD3Lt8wPC3ff1/4dSexej1ek4EfcOJoG8AcHQtT493fjA7t2cpSROlyvT/1ZA0YOHChRw+fBidTseQIUMIDQ3l4MGD2Nvb07BhQ/bs2cOff/7Jl19+iUajIS4ujqysLD744APatWtnGMAQEBDA7NmzOXbsGCtXruS7777j2rVrWFtb07RpUw4ePMiRI0cYNmwY/v7++Pr60rNnT1asWIGnpyfr169nw4YN2Nra4uLiQtOmTRk8eDD+/v5ER0ejVqupX78+d+7cYePGjUYDGM6ePcvcubnDMh9fv6nGfB3/QratOaLD4qROoVA16peXOoUCiduOF02V2sX3tuMNGxbP246P6WT+Om7fCzU5toZPRfPf0Az/aTEy1aRJk2jWrBl9+vSx+Lrv37/P0aNHefPNNwEYO3Ysb7zxBu3atbP4exVEFKOiEcWoaEQxKrrSXIxu3gs3ObaWj7Tftf93MzB4e3tz5coVunXrhkwmo2XLlrRt21bqtARBECyuJA3tLpbF6N9usBdBrVbzzTffvLD1C4IgFBfFYWCCqYplMRIEQRDMV5JaRiVnqIUgCIJQJC9qbrqgoCBef/11OnbsyPr16wuNO3LkiMnX40XLSBAEoZR6ES2j6OhoFi5cyLZt21Cr1QwcOBBfX1+qVatmFBcXF8fXX39t8npFy0gQBKGU0urlJj9MdfLkSZo3b46zszO2trZ06tSJvXv35oubNm0a/v7+Jq9XtIwEQRBKqaK0jJKSkkhKSsr3uqOjI46OeTPsx8TE4O6eN1WVh4cHly9fNvqbtWvXUrt2bRo0aICpRDESBEEopYoymm7NmjUsWbIk3+v+/v4EBAQ8ts78P019fALb27dvs3//fn755ReiokyfT1EUI0EQhFKqKC2jESNG0Lt373yvP94qAvD09OT8+fOG5zExMXh4eBie7927l9jYWPr27Ut2djYxMTEMHjy4wLsgPE4UI0EQhFKqKC2jJ7vjCvPKK6+wePFiNBoNNjY27N+/n5kzZxqWjx8/nvHjxwO5E2IPHz78mYUIRDH6z2VlvJgJOM2RY4nbYb4gSYnmTf//ohTXaXfGbxggdQoF+n3acalTKFR0bPH7TuYy/5bpuiIMTDCVp6cnEydOZPjw4WRnZ9OvXz/q16/PqFGjGD9+PPXq1Xuu9YpiJAiCUEq9qNtEdu/ene7duxu9tnLlynxx5cuX5/DhwyatUxQjQRCEUkpMByQIgiBIriRNBySKkSAIQiklWkaCIAiC5ETLSBAEQZCcVrSMBEEQBKmJbjpBEARBcgXM3FNsiWIkCIJQSunENSNBEARBaqKbTjBL/epq+ra3R6WAsOgcVu9MJiNLb3JM2yY2+DWyRqWU8SAyh9U7k8jRWia3JnXtGd7bHZVSRsjDTL5fG0l6Rv7feT8rzs1FyYJPKzN+5n2SUs1Lrm5VJT39bFApIDxWy69708jIMi1GJoOBHWyoXj73q3D1fjbbjmSYlc/jmjVwZOQb5VApZdwPy2DhqlDSCthehcXZ2sj54O2KVChnhUwm4+AJDVv+iLFIbvVXzSHl6h2CF/6cb5lHl9bUnP0hcrWa5Cu3uDx6CjnJqSCXU3vBZNw7tkSmVBC88GdCV2yySD5g/r6/6CM34pPz9qe9J9M4fcX8KaWqe8to31CBQgHR8Xp2ntaSVcgsQj1bKIhJ0HPqRt7n/FE/JclpeTEnr2u5EvLi+9BKUjddiby53saNG9m4cSMAkydP5uHDh0+NHzZsGGfOnDF6LTw83OTb4f6XHGxlvNXTkR+2JDLlBw2xCVr6dbAzOaZRLSvaN7NhwdoEPvtRg0oJrzW3tUhujvYKJowox5zl4Yz9IpiouCze7O1R5Li2zZ2Y+1ElXF3Mn3vL3kbG8M62rNiRyvRVycQl6OjlZ2NyjG9tNZ4ucmb+ksysNclUL6+kUQ3z8wJwclDw4TsVmLn4Pu9MuklUbCZv9fcqUtyIPuWI02Tz7tRbBEy/Tdd2brzkY97naV+rKr771+DVr0uBy9VuLtT/aQ4X+gdwtG5n0u6HUeurjwCoNHogdtUqcaxhN0606EeVgBE4NX2+ucieZO6+X9ZVQWqGjunL4w0PSxQiW6vcArPlWA4/7MwhIUVPh4b5D51ujjC8g4I6lYxbI66OkJEFy//IMTz+i0IEuaPpTH1IrUQWo0GDBjFo0CAAzpw5U+D9NUqqOj5q7j/MJkaTe3b357l0mtezNjnmlQbW7DuVRmqGHj2wbncyJy9b5kz/5dp23HmQQWRM7inhnqMJtPbNP8vv0+LKOClp3tCeGUvCLJLTS5WVhERpiU3IPQs99k8WzWqrTY6Ry0GtkqFUgEoBSoWMbK1l9qdGdR25FZxGRHRuM23X4Ue0a+FSpLil6x+yYlPuyZarsxKVSkZqunktyUpjhxC+ZhsRW/cUuNytY0sSz18h7e4DAB4s34jXoNx5yDx7diBszTb0Wi05CUlEbNmN9+AeZuXzL3P3/WoVVOh08PFwZ2aMKUN3P1tkFjjG+pST8fCRHk1y7vNzt3XUq5L/0Nm0ppx/7um49sB4/6ngJkOnzy1UY7oq8asnt0heptDrZSY/pPafd9Pp9XoWLFjAwYMHUSgUDBgwgJdeeomFCxeSkZFBYmIiH3/8MV26dGHSpEnIZDJu375NSkoKY8eOpVevXixevBgAKysrYmJiGD16NOvXr+f06dOsXr2ajIwMMjMzmTVrFk2bNn1mTnFxcUydOpWIiAiUSiUTJ07Ez8+PU6dOMX/+fACcnJz45ptvUKvVfPDBB8TFxQEwbtw42rdvb7HtU8ZRgSYpr3kfn6TD1lqOtVpm6Ip4WkxZVwX37eRMHOKEs4OCO6FZbDmQYpHc3F1UxGny+ibi4rOxs1FgYy036oJ7WpwmMYc5y57eki0KFwc58cl5752QrMPGSoa1GkNX3dNiTl3NolFNFXPHOiKXy7gRks2Ve5aZxdy9jPF2iNVkYWerwNZabtRV96w4nQ4+ebcirZo489ffiYRHmne2f21C7nT/ru2aF7jcpnxZ0sPzboqWER6FyskBpYMdNuXLkREembfsYRSO9Wqalc+/zN335XK4Hpy7v6uUMt4f7ERGpp4DZ9LNysvRTkZSal6BSUoDa7UMtQqjrro953LzqlLW+O/lcgiO1HHgbx1KBQxuqyAzW86Zmy9qGtM8Jek8/T8vRnv37uXvv/8mKCiI7OxsBg8ejIuLC7NmzcLHx4dTp07x1Vdf0aVLbhdCdHQ0mzZt4tGjR/Tp04dXX33VsK7Ro0ezadMmVqxYgZOTE5s2bWLZsmWUKVOGrVu3smrVKpOK0cyZM2nevDkjR44kLCyMQYMGsWPHDn788UemT59O/fr1Wbt2LdevXyc2NhZvb29WrFjBvXv32Lp1q0WLUWFnTLrH9qqnxSjkULuqmsWbEsnO0fNOL0f6trNn4z7zC5KskHa0Tqd/rjhLkBe6LUyL6fqKNSlpej75IQmVEsb0tqN9EysOnTe/e6ew99Xqih43b3ko3/8SzmcBlRnSqyzrtpt+B80ikxf8Aeq1ugKT1T/5H3pO5u77x/7O6wHI0erZfzqdDs1szC5GhbUZ9Cb+t/++qwdy/w9aHZy+oaNZLTlnbpqVlknEaLqnOHfuHF26dEGtVqNWqwkMDCQzM5M///yTvXv3cunSJVJTUw3xffr0QaVSUbZsWRo1asSFCxcKXK9cLueHH37g8OHD3L9/n7NnzyIv5Ev1pNOnTzNr1iwAKlSoQIMGDbh06RLt27fH39+fDh060L59e1599VVCQkL49ttviY6Opk2bNowbN878jfKYR4laqnrnXbNwcZSTkq4zOgN7WkxCso6LNzMNZ5KnrmTQw8+4370ohnR3o1kDBwBsreWEPMw7SLs6K0lO1ZL5xAXmWE02NSrbPDPOEjTJOiqXy9uNnR1kpD6xvZ4W83J1FZsPpaPVgTYLTl/NplFN1XMXo+G9y9L8ZScAbG3khITnHSDdXFQkp+SQmWV8FIvRZFPLx67AuMZ1Hbgfno4mIYeMTB1HTifQsonTc+VmqoywSJybNTA8t/b2JEuTgDYtnYywSKzKuuct8/Ik/aFlCqO5+36L+taERWUTHpPXjWmJOpmYpsfbLe9Y4mgL6Zl6sk3sLa1fRUZUvJ6YhP+9IAPdi28UASWrZfSfXzNSKo3rX3h4OIMHD+by5cvUrVuXMWPGGC1XKBSGf+t0unx//6/U1FT69u1LeHg4TZs2ZdiwYSbn9OQ1J71ej1ar5c0332TdunVUrFiR+fPns3TpUipXrsyePXvo3r0758+fp1+/fha9ZnXtXhZVy6vwKJP7/27TxIZ/bmaaHHP+RiZNaluh+t9mermWFfcjnv/mYeuD4pgw6z4TZt3no69DqFnVhnIeuQeDLn4unLmUnO9vLl5PNSnOEm6E5FDFS4G7c+6u3KqBFZfuZpscExqjpXGt3DzlcqhfTcn9iOfvplu7PYr3Pr/Fe5/fYsKXd6jlY4uXZ+71qa7t3Dh1MTHf31y4klxonF8zZ4b2yu33USll+DVz5p8blul2LUzsgRO4+DbAtlolACqOHkh00CEAooMOUeHNvsgUCpRODnj170p04EGLvK+5+763h4Jebe2RyUClhPbNbDh7zfzrpfci9JR3k1Em95yMJtXl3Aw3/Tvv4SyjbQMFMhkoFdCsppxrD/6baqTVyUx+SO0/bxk1bdqUtWvXMmjQIHJycnj77beJjIxkw4YNWFlZsXjxYrTavFOOPXv20LlzZyIiIrh8+TKzZ8/mxo0bhuUKhQKtVktISAhyudxQzKZNm2a0nqdp3rw5W7duNXTT/f3330yfPp033niDGTNm8Oabb+Ls7MyhQ4f49ddfCQsLY/Lkyfj5+dG2bVuSk5NNul2vKZLT9PwcmMS4NxxRKGTExmv5aXsSlcspebOHA9OXxxcaA3D4XDp2NjK+GF0GuQweROawxgJddACJyVoWrYlg8ujyKJUyomKz+HZ1BADVKlkTMKwcE2bdf2qcpSWn6Vm7J43RPe1QKCAuQccvf6RR0VPB0M62fLUmudAYgK2H0xnQ3oYv3nJAp4dbD3LYd9Yyd5dNTM7hm59C+cy/CkqljMiYTOavCAWgemUbJr5Vkfc+v/XUuBWbIhg/ojzLZ9dEr4eTfyeyY3+sRfJ7nFPjutRbPosTTXqRFavh0juTabz5e+QqFanBoVwa+SkAD5ZtxLZqRVpdCESuVhG6cjOa4+cskoO5+/7OI6kMed2BmWPLoJDDueuZRl13zystEwJPaXnDT4lCDvHJeraf1FKujIwezRUs/+PpJy9HLut4vamCsV2Vude1QnX/67p78UpSy0iml2Ao2sKFCzl8+DA6nY4hQ4YQGhrKwYMHsbe3p2HDhuzZs4c///yTL7/8Eo1GQ1xcHFlZWXzwwQe0a9fOMIAhICCA2bNnc+zYMVauXMl3333HtWvXsLa2pmnTphw8eJAjR44wbNgw/P398fX1pWfPnqxYsYLs7GyGDx/O4cOHiY6O5vPPPyciIveAOWHCBDp06MCpU6eYM2cOSqUSKysrZsyYgZeXFx988AGRkZEolUp69+7N8OHDTf6/vzXDMr8RsaTYiEdSp1Co8tXKSZ1CgYKvhkidQoHEbceLrpJP/hGOxcEXQ83/icG2s6a3wPo0k3ZwtSTFyFSTJk2iWbNm9OnTR+pULEYUo6IRxahoRDEqutJcjLaeMb0Y9fOVthiJGRgEQRBKqeLb1MivWBejuXPnSp2CIAhCiSWKkSAIgiC54jBKzlSiGAmCIJRSomUkCIIgSO4FTHrywohiJAiCUEoVhwlQTSWKkSAIQikluukEQRAEyYluOkEQBEFy/9WErJYgipEgCEIpJVpGQqEUKsWzg/5jOhMnlJWCh+fz3/7iRdLrKkmdQoGK67Q7fWe1kjqFQoXvviV1Ci+MuGYkCIIgSE500wmCIAiSK0ktI2mnaRUEQRBeGK3O9EdRBAUF8frrr9OxY0fWr1+fb/nBgwfp2bMnPXr04L333iMxMf9NJZ8kipEgCEIppdfrTX6YKjo6moULF7JhwwYCAwPZvHkzd+/eNSxPSUlh+vTprFixgp07d1KzZk3DPeieRhQjQRCEUkqvN/1hqpMnT9K8eXOcnZ2xtbWlU6dO7N2717A8Ozub6dOn4+npCUDNmjWJjIx85nrFNSNBEIRSqigDGJKSkkhKSsr3uqOjI46OjobnMTExuLu7G557eHhw+fJlw3MXFxc6dOgAQEZGBitWrGDYsGHPfH9RjARBEEqporR41qxZw5IlS/K97u/vT0BAwGPrzL9SmSz/HHjJycm899571KpVi969ez/z/UUxEgRBKKWK8qPXt0aMKLBoPN4qAvD09OT8+fOG5zExMXh4eBjFxMTE8Pbbb9O8eXOmTJli0vuLYiQIglBK6bSmV6Mnu+MK88orr7B48WI0Gg02Njbs37+fmTNnGpZrtVrGjBlDly5deO+990x+f1GMBEEQSqkXMR2Qp6cnEydOZPjw4WRnZ9OvXz/q16/PqFGjGD9+PFFRUVy/fh2tVsu+ffsAqFu3LrNnz37qekUxKobq+ajo09YWpUJGeEwOa3ankpGlNylmTB97PFzyphxydZJzOzSHH7YmWyS3pvXsGdG3LCqljJDwDL775SHpGfmvkhYWp1bJGDvEixqVbZDJ4Nb9dJaujyAr+/m/NdW9ZXRopEAph+h4PYGntGRmFxzb6xUFMQl6Tl7Py/mT/kqS0vJi/rqm5cp987/Fdasq6dXaBqVCxsNYLev2pJKRZVqMTAYDO9pSo0LuV/RqcDa//5ludk7/ql9dTd/29qgUEBadw+qdyfn2safFLPrIjfjkvGmk9p5M4/SVTMvlt2oOKVfvELzw53zLPLq0pubsD5Gr1SRfucXl0VPISU4FuZzaCybj3rElMqWC4IU/E7pik0XyCb56hBNB36DNycLNqyavDf4KKxt7o5jr5wI5f3AVMpkMpdqGtv2mUrZiPbQ5WRzeOouH93K7tqq85EerXh8jl7/4qcFe1I9eu3fvTvfu3Y1eW7lyJQD16tXj5s2bRV7nM4d2nzlzxqSREP+ftGvXjvDw8BeybntbGW92s2fp78l8tjyBuAQdfdramhyzbFsKX65K5MtViaz9I4X0TD0b9qVaJDdHewXvjyzPVz+G8u60O0TFZjGyr2eR4gZ0dUchB/8Zd/GffhcrlYz+r7vnW4epbK1yC8zmIzksDswhPkVPh0b5d2s3JxjRUUGdysYXWl0dIT0Llu3KMTwsUYjsbWQMf92OFTtSmf5TEnEJOnq3tjE5pnkdNZ5l5Hz5cxIzVydRvYKSRjVVZucF4GAr462ejvywJZEpP2iITdDSr4OdyTFlXRWkZuiYvjze8LBUIbKvVRXf/Wvw6telwOVqNxfq/zSHC/0DOFq3M2n3w6j11UcAVBo9ELtqlTjWsBsnWvSjSsAInJrWMzuntGQN+9ZPpvvbixn52T6c3CpwYucCoxhNdDDHd8ynz3s/MWxSIL6dxhL0U+5F/3+OrSc9WcOIybsYPmknEfcvcvvvPWbnZQqdTm/yQ2rid0bFTJ0qKkIic4iJzz1zP/J3Br511EWOUchhZHd7Nh9IIz7ZMhNUNapjz52QdCJick/vdx/R0MbXuUhxV2+nsWl3LHp9bhfCvbAM3F2f/yDr4yUj4pEezf8afudu6ahfJf9u3aymnIv3dFwLMf7SVXCXodfBm68pGNtdSev6cgoYGFRktauoeBClNXxGxy5m0qyOlckxMjlYqWQoFaBSgFIB2Tnm5wVQx0fN/YfZxGhyWzZ/nkuneT1rk2OqVVCh08HHw52ZMaYM3f1sLbLNACqNHUL4mm1EbC34YO3WsSWJ56+QdvcBAA+Wb8RrUO4ZumfPDoSt2YZeqyUnIYmILbvxHtzD7Jwe3DxB2Yr1cPGoDECDloO4cT7IaFSZQqmm46BZ2DvlXsgvW7EuqUlxaHOyaNxuJF3fWohMLic9NYHM9CSs7ZzMzssUL+J3Ri+KScVIo9EwatQoOnXqxJgxY8jKyuL333+nW7dudO/enUmTJpGamnv2XbNmTcPfbdu2jUmTJgG5rYn333+fTp06ERERwejRo+nTpw99+vTh0KFD+d5z0qRJTJ48mb59+9KpUyd27NgBQGpqKp9++il9+vShZ8+e7Nq1y/Bew4YNo3v37nz77beG9YwZM4ajR48CsHDhQt555x0gd7RHt27dANixYwe9e/emZ8+eTJkyhczM3LO8Y8eO0a9fP3r16oW/vz/x8fFGOd6/f5/XXnuNf/75x5TNaBIXRznxSXnFIz5Jh621HGu1rEgxLRtakZCs4+LtJ/qFzOBeRkWsJq//Ky4+GztbBTbWcpPjLl5PISI6yxDXs4MrJ87n/22DqZzsZCSm5n2TktLAWi3D6on69sdZHZeD83/jFHK4F6lj3UEtq/fmUM1Lhm8t88/RXBxkxp9Rsg4bKxnWatNiTl3JIi1Dz9xxTnw9zpmYeB1X7hXS91hEZRwVaJ6x/zwtRi6H68FZLFyfwNzV8dT1UdOhmXGr73ldmzCTh+sDC11uU74s6eFRhucZ4VGonBxQOthhU74cGeF5P67MeBiFjXdZs3NKjo/CwSVvPQ7OZcnKSCErI6/Hwcm1PFXrtgFyhz4f2TYHn3rtUChzP3CFQsXxwAX8PKMjtg5uePs0MTsvU5S6YhQREcHnn3/Onj17iIuLY+PGjSxbtox169YRFBSEjY1NgePTn+Tn58e+ffs4c+YM3t7ebNu2jfnz5xsNE3xcdHQ0mzZtYs2aNcybN4/Y2FiWLl1KnTp12LZtG+vXr2fZsmWEhYUZ4rdv384HH3xgWEfr1q05ffo0AOfOnSM4OBitVsvx48fx8/Pjzp07bNmyhU2bNhEYGIirqyurVq1Co9HwzTffsGrVKnbs2EHLli1ZsCCvaR4ZGYm/vz9z5syhYcOGpmxGk8gLOcXUPba3mBLTsakNu/+y3DUGKPi3BEC+Jr4pcdUqWTPv0yrsOqzh3OXnv55V2Am5qb0OF+7o2XNOh1YHGdlw8rqOlyqYf5pf6DbQmxbT7VVrktN0fLI4kUk/JmBnLaNDU6sC44ueW8GvP77/PC3m2N8ZbNibQo4W0jP17D+dTqNalsntmeQFH7L0Wh3I8yetL+qkawWtW1/wOuQF5JKdmcaunyeQEBdKx0GzjJa16vkR7807i2MZbw5tnm52XqbQavUmP6Rm0gCGWrVqUaFCBQB8fHxITk6mbdu2uLi4ADBgwAAmT578zPU0aNAAgJdffplvv/2W6Oho2rRpw7hx4wqM79OnDyqVirJly9KoUSMuXLjAyZMnycjI4PfffwcgLS2NO3fuAFC7dm2USuP/Ups2bRg7diwpKSlAbsvt2rVrHDt2jKFDh3LmzBkePHhA//79gdypLGrXrs2lS5eIjIxk+PDhAOh0Opyc8prW77//PvXq1aNx48ambEKTPUrUUsUr7//g7CAnNV1HVrbpMRU8FcjlcDvU/H6doT098G3gAICtjYKQ8AzDMldnFcmpOWQ+ceE7VpNFzSo2hcb5NXXivaHlWLo+kqNnnz2B4tMkpuop7553UHCwhbRMvcldWvWryojW6IlOyH0uAyzxvdQk6ajilXeBuqDP8WkxDWuo2XwwLXcSyyw4fTWLl2uqOHjO/GszjxK1VPXOazq6OMpJKWAfKyymRX1rwqKyCY/JG8BggWO+STLCInFu1sDw3NrbkyxNAtq0dDLCIrEqm3f90drLk/SHUQWtpkgcypQj6sElw/OUxGisbJ1QWRlfy03SRLBj+Rhcy/rwRsBaVOrcbs2HwRewtS+Di0cVFAoVdZr35vBvxoXqRSnKnHNSM6ll9PgBXiaT5RuLrtfrycnJMXoOGL0GYGWVe/ZUuXJl9uzZQ/fu3Tl//jz9+vUrcKMpFHlfVJ1Oh1KpRKfTMX/+fAIDAwkMDGTLli20apV74y5ra+t86yhXrhw6nY79+/fTqFEjfH19OX36NNeuXaNRo0ZotVq6dOliWN9vv/3G559/jlarpVGjRobXt27dyvfff29Y79SpUwkNDTV0AVrK9fvZVPVW4uGS+9G0bmTNP090tT0rpkZFFTcfWKZL59fAGAK+vEfAl/f44Kt71PSxxcsjt+vh9TZlOP1P/lbN39dSCo17tbEj7w4qx7RvQ8wuRAD3IvWUd5NRJrde0rSGnFthpn8BPZ1ltG2oQCbLvS7TrJacayHmH1lvhGRTxSvvM/JrqObS3WyTY8Kic2hcK3f7yeVQv5qK+xGWuQnitXtZVC2vwqNM7verTRMb/rmZaXKMt4eCXm3tkclApYT2zWw4ey2D/0LsgRO4+DbAtlruzQ0rjh5IdFBuN3900CEqvNkXmUKB0skBr/5diQ48aPZ7Vq7VksiQS8THhABw6cQmqtVrbxSTnprAlkVDqd7gNbqOXGgoRABht09z5Pc56LQ56HU6bpwLomINX7PzMoVOZ/pDas/dOX748GESEhIA2LJlC76+uRvXxcWFO3fuoNfrOXz4cIF/++uvv7J48WK6dOnCF198gUajITk5/0Ftz5496PV6Hj58yOXLl2ncuDHNmzdn48aNQO51nx49ejxzEj4/Pz+WLl1Ks2bNaN68OevWraNBgwYoFAp8fX05cOAAjx49Qq/XM336dNasWUODBg34559/uH//PgA//vgj8+bNM6yzfv36TJ8+nRkzZpCWllbYWxdZcpqe1btSGNPHgS9HO1HeXcGWQ2lUKqvg87ednhrzL88yCh4lWn7vSkzW8t3qcCaPrcCymdWo7G3FT1tyzzyrVbJm8ec+z4wb0ccTmQwmjPBm8ec+LP7ch7GDyz13TqkZsOOklgGtlfj3UOLhImPfeS1erjLGdHt2w//IJR3pWfBedyXvdVcSFqvnwh3zzyaT0/Ss/SOV0b3s+OIdR7zcFWw9nE7Fsgqmvunw1BiA3w6lY2MlY/o7jkwb6Uh8so59ZyxzwE9O0/NzYBLj3nBk1ntlKO+hZPP+FCqXUzL9XZenxgDsPJJKarqOmWPL8OWYMtwNy+bY3y+uGDk1rkvL8zsAyIrVcOmdyTTe/D2tL/+BQ90a3Pj4awAeLNtIWnAYrS4E0vLUVsJWb0Vz/JzZ72/r4MprQ+YQtGo8v8zqQlzEbfx6f0pU6BXWze0JwOUTG0mOj+Tu5QOsm9vT8EhPjadph1E4lvFi3dyerJ3bE7lCQcseH5qdlylexKzdL4pM/4wszpw5w5IlS1i3bh2QO7CgWbNmaLVa1q5dS3Z2NnXq1GHGjBnY29vz22+/sXTpUtzc3GjcuDHx8fHMnTuXdu3asXbtWsqXL09KSgoffPABkZGRKJVKevfuzfDhw9m4cSMxMTFMmDCBSZMmodFoiIuLIysriw8++IB27doZpie/efMmWq2W0aNH07t3b7Zt28bZs2eZO3cuAD179mTFihWGqStGjBjB2bNnsbW1pUWLFkydOtUwTv63335jzZo16HQ6XnrpJb766iusrKw4fPgwixYtQqfT4enpyfz583FxcTH6v0yePBkHBweTp7wY9dUjcz6vFyIi+Nkz6kqliV/NZwdJIDoyReoUCpSVYZkWsaWJ244X3buvmb+Oab+YPoBp1pvqZwe9QM8sRlL5t+j16dNH6lQsShSjohHFqGhEMSq60lyMpqwy/RrjV2//R4NQCiFmYBAEQSilisOPWU1VbIvRv91tgiAIwvMpph1fBSq2xUgQBEEwTyE/kSqWRDESBEEopXSiZSQIgiBITXTTCYIgCJIrDtP8mEoUI0EQhFJKL0bTCYIgCFIT14wEQRAEyYmWkSAIgiA5UYyEQjVr5ip1CvnsfBAjdQqFatmgeP5Q4p6ni9QpFCg6tnhOB1Rcp9wBKN+1eE45Rbb520wMYBAEQRAkJ4Z2C4IgCJITc9MJgiAIkhMtI0EQBEFyYgCDIAiCIDlRjARBEATJabXFczRqQUQxEgRBKKXENSNBEARBcmI0nSAIgiA5cc1IEARBkJzophPMcu/qEY4HfoM2Jwt375p0GvIVVjb2RjHXzwZy7uAqQIZKbUO7N6ZStlI9tDlZHNoyi/B75wGoUtuP1r0/Ri5XWCS3JnXtGd7bHZVSRsjDTL5fG0l6Rv6LpM+Kc3NRsuDTyoyfeZ+kVK1ZOV39+xg7NywiJzsL70o1GDxmBja2xtvr6N6NHN+/BZkM3DwrMPjdL3BwMp6aaeWCiTi5uNP/7Slm5fOv4GtH+Cso93N086pJx0H5P8cb5wI5f2gVMpkMpcqGNv2mUrZiPfQ6HceDFnD/2lFkMjku7pVoP+BLbB3KWCS36t4y2jdUoFBAdLyenae1ZBUyk1DPFgpiEvScupH3+X3UT0lyWl7MyetaroSYf+ALvnqEE49ts9cGF7Dvnwvk/MH/bTO1DW3/t820OVkc3jqLh//u+y/50aqX5fZ9gPqr5pBy9Q7BC3/Ot8yjS2tqzv4QuVpN8pVbXB49hZzkVJDLqb1gMu4dWyJTKghe+DOhKzZZLKen0etKzgAGudQJmOPKlStMnTpV6jQsKi1Zw951k+k5ajFvf7EPJ7cKHAtcYBSjiQ7m6Pb59B33EyOmBNK881gCVwYAcPHoetJTNIycuos3p+wk4v5Fbv29xyK5OdormDCiHHOWhzP2i2Ci4rJ4s7dHkePaNndi7keVcHVRmZ1TcpKGX3/8jHc+/JbPFwXh6lGenRu+M4oJDb7OoaA1fDhrLVO/2Y572Yrs2vyDUcyBwJ+5d+Nvs/P5V1qyhv3rJ9PtrcW8OW0fTq4VOBGU/3M8Fjif3mN/Yuingfh2GsuuVbmf49XTvxMTdo0hH29n+OQgnNwrcmzHXIvkZmuVW2C2HMvhh505JKTo6dAw/6HAzRGGd1BQp5LM6HVXR8jIguV/5BgelihEacka9q2fTPe3FzPys9x9/8TO/Nvs+I759HnvJ4ZNyt1mQT/lbrN/jq0nPVnDiMm7GD4pd9+/baF9375WVXz3r8GrX5cCl6vdXKj/0xwu9A/gaN3OpN0Po9ZXHwFQafRA7KpV4ljDbpxo0Y8qASNwalrPInk9i1arM/lRFEFBQbz++ut07NiR9evX51t+48YN+vbtS6dOnZg6dSo5OTnPXGeJLkb16tVj9uzZUqdhUSE3TlC2Uj1cPCoD0LDVIG6cCzJqbiuUal4bMgt7p9wDvGeluqQmxaHNyaJJ+5F0e3shMrmc9NQEMtOSsLZ1skhuL9e2486DDCJjck+h9xxNoLWvY5Hiyjgpad7QnhlLwiyS081Lp6jkUxePcpUAaPVaf84d/8Noe1WsWpsvFgVhY+tAdlYmiZoY7Ozztsntq2e58c9ftOz4hkVyAnhw8wRlK+Z9jvVbDuLm+fyfY8dBj32OFfM+R9dy1WjV8xOUKnXusgp1SdJEWCQ3n3IyHj7So0nOfX7uto56VfIfCprWlPPPPR3XHhgXmgpuMnT63EI1pqsSv3pyZLJ8f15kT26zBi0HceMZ26zsY9uscbuRdH3rsX0/PQlrO8vs+5XGDiF8zTYithZc3Nw6tiTx/BXS7j7I/b8s34jXoO4AePbsQNiabei1WnISkojYshvvwT0sktez6HV6kx+mio6OZuHChWzYsIHAwEA2b97M3bt3jWI+/vhjPvvsM/bt24der2fLli3PXG+J7qY7c+YMS5YsAXIL04ULF9BoNEybNo3WrVvz8OFDJk+ejEajwdramlmzZlGrVi1+//13Vq9ejUwmo06dOnz22WfY2dnx6quv0rZtW86fP4+7uzuDBw9m3bp1REVFMXfuXJo1a8aDBw+YPn06CQkJWFtb89lnn1G7dm2L/Z+SE6JwdClreO7gXJasjBSyMlIN3RVOruVxci0P5PYJH/l9Dj712qFQ5h64FAoVx3Ys4OLR9XhWqkv5ak0skpu7i4o4TV5fTlx8NnY2Cmys5UZdcE+L0yTmMGfZQ4vkAxD/KApn17zt5ezqSUZ6ChnpqUZddQqliktnD7Nh+XSUSjVdB4wDIEETw9Zfvmbc1GWcOPCbxfJKTojCvoif49Htc6haN/dz9KrysuFvM9ISObPvR+q/OtAiuTnayUhKzTv4JKWBtVqGWoVRV92ec7mfaZWyxn8vl0NwpI4Df+tQKmBwWwWZ2XLO3DSvSyg5PgqHou772/Lv+8cDF/DPsfV4VqyLt49l9v1rE2YC4NqueYHLbcqXJT08yvA8IzwKlZMDSgc7bMqXIyM8Mm/Zwygc6/03M4W/iAEMJ0+epHnz5jg7OwPQqVMn9u7di7+/PwAPHz4kIyODhg0bAtCnTx++//57Bg8e/NT1luiW0eOys7PZvHkzkydPZtGiRQDMmDGDTp06sWvXLgICAli6dCm3bt1i2bJlrFu3jqCgIGxsbAwFLS4ujjZt2rB3714ADh48yIYNGwgICGDNmjUAfPrpp3z88cds376dmTNnMnHiRIv+Pwrr45XJ839UWZlpBK2aQEJsKJ2GzDJa5tfrI/wXnMWpjDcHNk23SG6yQvaWJ4ePmhpnCYVtL3kB26tBs3Z8veoYr78xhh9mjyE7O4vViz6h75uf4OTibunETM4rOzON3atzP8eOg4w/x4TYULYsGopX1UY0aDXEIqkV1ogpJOV8/r6rZ+95HVodZGbD6Rs6alUwv2mkL+I22/XzBBLi8m+zVj0/4r15Z3Es482hzdPNzsskBeQIoNfqQJ5/2+j/ox+j6vQ6kx9JSUmEh4fneyQlJRmtMyYmBnf3vO+Lh4cH0dHRhS53d3c3Wl6YEt0yelyrVq0AqF69OgkJCQCcO3eOb7/9FoDWrVvTunVrfv31V9q2bYuLS+79aAYMGMDkyZMN6/Hz8wPA29ubxo0bA+Dl5UVSUhKpqalcvXrVKD4tLY34+HjD+szlWKYckSGXDM+TE6KxtnVCbWVrFJekiWD7sjGUKetD/wlrUamtAXh47wI29mUo41kFhUJFnea9Ofyb8Ze1KIZ0d6NZAwcAbK3lhDzMNCxzdVaSnKolM8u4yMRqsqlR2eaZcZbg4laOkLtXDM8TNTHY2jliZZ23vWKjQklKiMOnViMAWrTrzaaVswi9d41HMQ/Ztib3ukRSQhx6nY7s7EyGjJlhVl4OLsafY0piNFa2TqgK+BwDV+R+jm8ErEX5v88RIOz2aXb/MpEm7d+hSfu3zcrncYlperzd8g6ejraQnqkn28RxJPWryIiK1xOT8L8XZGCJ6+QOZcoR9cC0bbZj+Rhc/7fNDPt+8AVs7cvg4mGZfb8oMsIicW7WwPDc2tuTLE0C2rR0MsIisSqbd3C29vIk/WFUQauxuKK0jNasWWM4MX+cv78/AQEBeessYISe7LF+2mctL0ypKUZWVlaA8X9aqcz77+n1eu7du4fuiW+NXq83urimVqsN/1YojEfh6HQ61Go1gYGBhteioqIMzVVLqPRSS45s+5r4mBBcPCpz6cQmfOq3N4pJT01g03dDqevbh1e6+hstC719moj7l+j97o/IZHJunAuiYg3f585nfVAc64PiAHByULD486qU81ARGZNNFz8XzlxKzvc3F6+n8lY/z2fGWcJLDVqwfd0CYiIf4FGuEscP/Ea9pm2NYhLjY/ll0adMmvcb9o4unDu+G6+K1fCp9TKzlh4wxO3e8iOpyQkWGU1XqVZLju3I+xwvn9iETz3jzzEjNYHfvh9Kbd8+tOhi/DlGBP9N0Cp/Xh/xLZVr+5mdz+PuReh5rZGMMg6gSYYm1eXcDDf9oOXhLOOlinK2HNOikEOzmnKu3De/GlWu1ZJj2433/Wr18u/7WxYNpY5vH1q8brzNwm6fJvL+JXqOtsy+XxSxB07w0rxPsa1WibS7D6g4eiDRQYcAiA46RIU3+xKz608U9rZ49e/KlXFf/Cd56YrQAhsxYgS9e/fO97qjo/F1YU9PT86fP294HhMTg4eHh9HyuLg4w/PY2Fij5YUpNcWoIE2aNGH37t0MGDCAkydPsmTJEr744gvWrl3Le++9h7OzM1u2bMHX17Qd1sHBgcqVKxMYGEjPnj3566+/+Pzzzzl48KDFcrZzcKXz0Dns/Gk82pxsnN0r0mX410Q9uMK+9dMYMSWQS8c3kqyJ5M6lA9y5lHcw7T/+F5p1HMXhrV+x5queyORyvKs2olXPDy2SW2KylkVrIpg8ujxKpYyo2Cy+XZ17Ub1aJWsChpVjwqz7T42zNAcnV4aOncmqbz8kJycbN88KDPefzYN719iwbDqT5/9GtZca06nPKBbNeAu5XIlTGXdGffzdC8nnX7YOrrw2eA67fh6PTpuNk1tFOg/9mqjQKxzcOI2hnwZy6cRGkuMjuXf5APcu532Off1/4dSexej1ek4EfcOJoG8AcHQtT493fijsLU2WlgmBp7S84adEIYf4ZD3bT2opV0ZGj+YKlv/x9JFPRy7reL2pgrFdlcjlcD1Ux993zW/12jq48tqQOQStemybDcvdZgc2TGPYpEAu/2+b3b18gLuPbbN+Ab/QtMMojvz+Fevm9gSZHG+fRrTsYZl9vyBOjetSb/ksTjTpRVashkvvTKbx5u+Rq1SkBodyaeSnADxYthHbqhVpdSEQuVpF6MrNaI6fe2F5Pe7Jk++ncXR0zFd4CvLKK6+wePFiNBoNNjY27N+/n5kzZxqWe3t7Y2VlxYULF2jcuDE7duww9Dg9jUxfkn4V9YTHBzD4+/vj6+tLeHg4w4cP5/Dhw0RGRjJt2jTi4uKwsbFh1qxZVKtWjd9++421a9eSnZ1NnTp1mDFjBvb29tSsWZNbt3Jv9Ttp0iSaNWtGnz59DO+zbt067t27ZxjAoFKpmD59OvXr1zc555WWq1sWs/O3G1KnUKjx71WVOoUC3YuykjqFAhXX246X9TB/GP+LUlxvO97VArcd7zbqusmxu1aaPhArKCiI5cuXk52dTb9+/Rg1ahSjRo1i/Pjx1KtXj5s3bzJt2jRSU1OpXbs2c+bMMep1KkiJLkYlkShGRSOKUdGIYlR0pbkYdX3nqsmxu3+qa/b7maNUd9MJgiD8fybmphMEQRAkJ4qRIAiCIDmt1rx5H/9LohgJgiCUUiVpolRRjARBEEop0U0nCIIgSK6wKZaKI1GMBEEQSilx23FBEARBcuKakSAIgiA5nRhNJwiCIEitJA1gENMBCYIgCJIrNTfXEwRBEEouUYwEQRAEyYliJAiCIEhOFCNBEARBcqIYCYIgCJITxUgQBEGQnChGgiAIguREMRIEQRAkJ4qRIAiCIDlRjARBEATJiWIkCIIgSE4UI8FsM2fOzPfap59+KkEmJcudO3c4f/48586dMzyKm5SUFKlTMLh8+bLR84yMDObOnStRNvklJiZKnUKJJmbtLkESExOZP38+oaGhLFq0iHnz5jFp0iScnJwkyWfq1KmEhYVx9epV7ty5Y3g9JyeH5ORkSXJ60vnz51mzZk2+A8XatWslyijXjBkz+PPPP6lQoYLhNZlMJnlef/75J+fPn+e9996jX79+aDQaxo8fz5AhQyTNC+Djjz/m66+/pmHDhhw9epQZM2bQvHlzqdPixo0bTJw4kYyMDDZv3szQoUP57rvvqFOnjtSplSx6ocQICAjQb9q0Sd+9e3d9Zmam/ttvv9WPGjVKsnzCwsL0p0+f1nfv3l1/5swZw+P8+fP6+Ph4yfJ6XPv27fXbt283yu/MmTNSp6Xv2LGjPj09Xeo08unTp4/+7t27+i1btug/+eQTfUpKir53795Sp6XX6/X64OBgfdeuXfVjxozRd+3aVX/27FmpU9Lr9Xr94MGD9Xfv3tX37NlTr9fr9SdOnND37dtX2qRKINFNV4KEh4czYMAA5HI5arWaiRMnEhUVJVk+5cuXx9fXl6VLl1K+fHnDw8vLC10xucOkp6cnvXr1olmzZkYPqVWoUAF9Mb17i4+PD0eOHKFdu3bY2dmRnZ0taT4RERFERERgZWXF9OnTuXLlCv7+/nh7exMRESFpbgDp6en4+PgYnr/66qtkZWVJmFHJJLrpShCFQkFycjIymQyAkJAQ5HLpzyf8/f25ffs2NWvWRK/Xc+fOHdzd3VEoFMycOZMWLVpIltuwYcP46KOPaN68OUpl3u7eq1cvyXICcHJyomvXrrz88suo1WrD63PmzJEwK3Bzc2PmzJlcvXqV+fPnM3fuXLy8vCTNaejQochkMkPxVqvVzJs3D8jt2jx06JCU6eHs7MzNmzcN38udO3dK1nVekomb65Ugx44d49tvvyUyMpLGjRvzzz//8NVXX9GmTRtJ8xozZgz+/v7UrVsXgFu3brFkyRKmTJlCQEAAW7dulSy34cOHA+Dt7W30utQH/e3btxf4eu/evf/jTIylpKRw8OBBXn75ZSpVqsT69evp2bMn9vb2kuZVnIWGhvLpp59y5coVrK2tqVSpEvPnz6dq1apSp1aiiJZRCeLn50fdunW5fPkyWq2WL7/8Ejc3N6nT4uHDh4ZCBFCzZk1CQ0MpV64cWq1WwswgNjaWPXv2SJrD42JjY3F3d8fX11fqVAqUk5ODh4cHlSpVYvny5Vy7dg1fX1+qVasmdWoFDuCZPHkyjo6OkuZVsWJFNm7cSFpaGjqdThTu5ySKUQmyZMkSo+c3b97E2toaHx8fSVtHFSpUYMGCBfTs2ROdTseuXbuoVKkSFy9elLwbsUmTJvz555+0atXKqJtOKtOmTWP58uX5up6geHQ5ffjhh7Rt2xaAvXv3MmLECL744gvWr18vaV4An332Ga+++iqXL1/Gzs4ODw8PPvroI1asWCFpXsV1xGZJI7rpSpBPPvmEBw8e0LVrVwD279+Pvb09crmcypUr88knn0iSV0pKCj/88AN//fUXCoWCV199lTFjxnD48GGqVq1q1Gr6r7Vs2ZK4uDij12QyGTdu3JAoo+KtX79+bN26lZkzZ1KpUiWGDx9Onz592LZtm9SpGfLo1asXO3bsAKBHjx7s3LlT0rw6dOiAv79/vmtrxWGgTEki/amiYLL79++zfv16wwXvgQMHMmzYMDZv3kyPHj0kK0abN2+mX79++X7o2qNHD0nyedyJEyekTqFAwcHBbNiwgbS0NPR6PTqdjvDwcMlbIDqdjqtXr3Lw4EF+/fVXbty4IXlX67+K6wCef0dsCuYRxagESUpKIicnx1CMsrKySE1NBZB0mLBWq2X69OnExcXRsmVL2rZtS7NmzYpFt9iTXZv/8vf3/48zMTZx4kTat2/PhQsX6N27N8eOHaN69eqS5gS5PyydN28eb731FhUqVKB///5MnjxZ6rQAGD9+PMOGDSMyMpL33nvPMIBHasV1xGZJI7rpSpC1a9eyceNG2rRpg06n49ixYwwbNoysrCyuXLnCN998I2l+KSkpBAUFsXTpUlJTU7lw4YKk+YBxMcrOzub48eM0aNCAL774QsKsoHv37gQFBfHtt9/i5+dH/fr1GThwYLHoDnv06JFhkEzDhg2LxSCZf2k0GkNuDRo0KBa5FdcRmyWN9KeugskGDBhAUlISMpkMR0dHBg0aRGxsLD179mTw4MGS5bVnzx7OnTvH+fPnUSgUdOnSpVhM0wL5W0Djxo3jrbfekiibPDY2NmRlZVG5cmWuXbtGkyZNyMzMlDotjh8/zpQpU2jYsCE6nY7PP/+c2bNnGwY1SCkrK4stW7YQHBzMZ599xpo1axg9erTR77SkUNxGbJZUohiVIAEBAaSnpxMaGkqTJk04d+4cDRs2pHLlypLmNWfOHLRaLSNGjKBjx45UqVJF0nyeJjU1tVj8ar9Hjx6MGTOGBQsWMGDAAI4fP46np6fUabFw4UI2bNhgmDMvLCwMf3//YlGMvvzyS8qUKcP169dRKpWEhoYydepU5s+fL2lexW3EZkkltlwJcv/+ffbv38/s2bPp27cvn3zyCRMmTJA6LY4dO0ZwcDCnT59m0aJFhISE4OPjI3m3IUC7du0MF7z1ej1JSUnFomU0dOhQevXqhb29PevWrePKlSu0bNlS6rTIyckxmry1QoUKxWZqp2vXrrF9+3aOHTuGjY0NX3/9Nd27d5c6Lf78809+++03o9fEiM2iE8WoBHF1dUUmk1GlShVu3bpFr169is0cWDqdjpycHDIyMsjIyMDGxkbqlABYt26d4d//dm8Whx8lPjmwQiaTERoaKvlvxry8vPjll1/o168fAFu3bs13LUQqMpmMrKwsw8lFfHy84d9SKq4jNksaUYxKkOrVqzNz5kwGDRrERx99RExMjOSTWAK0atUKb29v/Pz8CAgIKFZT53t6enLixAkSEhKMXpd6pFNoaGiBvxm7cOECZ8+elWyY/uzZs5k5cybLli1Dr9fTvHlzvvzyS0lyedLw4cMZOXIksbGxzJ49m4MHDzJu3Dip0yI9PZ0lS5Zw6tQptFotzZs3Z8KECdja2kqdWokiRtOVIFqtlosXL9KkSRMOHTrEqVOn6N+/PzVq1JA0r/j4eFxcXCTNoTATJkwgIiICHx8fo7NoqUc6vfHGG0a/GcvKyjL6zZjUP+Qsru7evcuZM2fQarU0a9aMWrVqSZ0SkydPxsbGhv79+wOwZcsWkpOTJb+WVdKIllEJolAoaNKkCQDt27enffv2kuZTq1atArtJ9Hp9sekzv3XrFnv37pU6jXye/M1YdnY2aWlpgDS/GXv82lpBpJ6mCHKHw7du3Zo2bdrQuHHjYtFFB7nXsh4/efj88895/fXXJcyoZBLFSHhuN2/elDqFZ/Lx8SEmJgYPDw+pUzEyZMgQ+vbtS5s2bdDr9Rw9epShQ4fyyy+/SNLSffzaWnH1888/c/z4cX799VemTJlC/fr1adeuneQH/n8Hxvw7YWtSUhIKhULSnEoi0U0nmC0pKYmgoCASEhKMzuqlnuUA4O233+bixYvUqFHD6PcoxWESy1u3bnHq1CnkcjktWrSgevXqhISE4OXlJflvZx7370zjxYFer+fq1aucOnWKX3/9lZycHE6ePClpTr///jvLly+nXbt2ABw+fJjRo0cbBoEIphHFSDDbyJEjcXBwoHr16kZdJ8WhGJ09e7bA14vjJJbF6aD/uMcnJpXSqFGjCA4OplatWjRr1gxfX99icc0I4Pbt25w7dw6dToevr6/k13FLItFNJ5gtLi6O1atXS51GgYpj0SnMqFGjisVB/0nFJafatWuTlpZGQkICjx49Ii4ujoyMDKytrSXN69atWyxbtoyFCxdy7949Pv/8c2bOnClurldEomUkmO2TTz7hrbfeKjZnqc/Su3fvQu+0+v/VuXPnnrq8adOm/1Emz5aamsr+/ftZunQpERERXL16VdJ8+vfvj7+/P35+fgD89ddfLFmyhI0bN0qaV0kjWkbCc/vjjz94/fXXuXDhAkFBQbi6umJlZWUYTVccRmAVZPny5ZK9d3E96H///feFLpPJZMXiGtvx48c5deoUp0+fRqvV0qlTJ1q3bi11WqSnpxsKEcCrr74qhnU/B1GMhOf2/fff89prr5GUlMShQ4cMRai4SUlJITk5WdLbbPyruB70S8JouvXr19OmTRuGDx9O2bJlpU7HoEyZMmzcuNFw/64//vgDV1dXibMqeUQ3nfDcJk+ezI4dO/IVoeL0O6Nly5axYsUKnJ2dDa8V51ab1M6fP8+qVauMbvoXERHB4cOHpU6tQMVh0EdERAQzZszg7NmzqFQqmjZtymeffVasCmZJIIqRYLaxY8eydOlSqdMoUIcOHdiyZQtlypSROhUjxfWg37lzZ0aNGsX27dsZNmwYx44dw87OjilTpkiaV2GKy0g/wXzS37NXKPGKayECKFeuHE5OTlKnkc+0adPo0KEDWq2WIUOGUKlSJTp06CB1WlhbW9O3b1+aNWuGo6Mjs2bNeuZ1rv/KwoUL871WHArR8ePH6du3Lx06dDDMjCL17CglkbhmJJRqlStXZvDgwfj6+hr9kFTq30D9e9B/+PCh4aDfp08fSXMCsLKyIiEhgSpVqnDp0iVatGhhmKZIan/++Sfvv/9+sbsuOWvWLCZNmpTvd3ZC0YhiJJRqnp6exeKmdU8qrgf9N998k4kTJ7J48WL69etHUFAQdevWlTotAJydnencuTN16tTBysrK8LrUk966uLgUi5sPlnTimpFQ6mk0Gi5duoRWq6Vhw4a4ublJnRJ79uxhy5YthoO+QqGgVq1akt+QMDExEUdHR2QyGWlpaYSEhODg4GB0wz2pFPbbsN69e//HmRibP38+OTk5tGrVyqhIFqffZpUEohgJpdrx48eZMmUKDRs2RKfTcfHiRWbPni35mWxxO+hHRkai1+sZPXo0K1euNAyD12q1jBo1qtjMfB4eHs7du3dp2bIlkZGRxaJIDhs2rMDuueLw26ySRHTTCaXawoUL2bBhg+GgFRYWhr+/v2TFqLCDvoODg6QH/e+//54zZ84QExPDkCFDDK8rlUpJ7zz7uD/++IOlS5eSkZHBpk2bGDhwIJ988gk9e/aUJJ/PPvuMmTNnAvlv+yGuHRWdKEZCqZaTk2N09lyhQgV0Op1k+RTXg/6/111WrFjB6NGjJcvjaVauXMnGjRsZOnQorq6ubN++nZEjR0pWjAYMGABAQECAJO9f2ohiJJRqXl5e/PLLL4bp/Ldu3Yq3t7dk+RTXg/7mzZsZMGAAWVlZLFmyJN9yqUcfAsjlcuzt7Q3PPTw8kMul+3XKvwM7StJkvMWZKEZCqTZ79mxmzpzJsmXL0Ov1NG/enC+//FKyfIrrQb8kXDquXr264R5GN27cYMOGDSVmcl7h2cQABkH4D/17raOgQgTFowXy6NEjLly4YLjNfXH50XBaWhpLly7l5MmT6PV6fH19GTdunFFrSSi5RDESSqV3333XcPfNgi4mF4e56YrjQX/nzp18/fXXNG7cGK1Wy+XLl5k1a1axmB0bcrfZ+fPnUSqVxWabCZYhipFQKsXExODh4cHDhw8LXC7ldSMovgf9zp07s2bNGsMPhR8+fMiYMWMICgqSNC+AwMBA5s2bV+y2mWAhekEoxfz9/fO9Nnz4cAkyMdapUyd9VFSU4Xl4eLi+W7duEmaUq2/fvnqtVmv0Wu/evSXKxlhx3WaCZYgBDEKpNG7cOG7evElMTIzRpJVarbZYTO1vb29vdOsDb29vVCqVhBnlqlGjBqNGjaJv374oFAr27NmDh4eHYULSXr16SZZbcd1mgmWIbjqhVEpJSSEhIYHZs2czbdo0w+tKpRJXV1eUSmnPw6ZMmUJ0dLTRQT8jI4POnTsD0h30J0+e/NTlUs4DV1y3mWAZohgJpd7169cN9w3SarWEh4cbfnckleJ80H9SRkYG1tbWUqdRoraZUHSiGAml2qeffsrFixdJTEykatWq3Lx5k0aNGrFq1SqpU8unOBz09+3bxw8//GB007/09HROnz4taV5C6SeuGQml2rlz59i3bx8zZ85k+PDh6PV6SX/0+q/ietCfP38+s2bNYvXq1YwZM4YTJ04QHx8vaU5Ps3jxYjEdTykh7vQqlGoeHh6oVCp8fHy4desW1atXJzU1Veq0mD9/PlOmTMHHx4cFCxbQp08fXn/9danTwtHRkebNm9OgQQOSk5MJCAjgn3/+kTqtQj0+oEEo2UQxEko1T09Pli9fzssvv8ymTZvYvXt3sbiJXXE96FtbW3P//n18fHw4e/YsWVlZJCcnS51WgVJSUhg4cKDUaQgWIoqRUKrNnj2b8uXLU79+fV577TV2797N9OnTpU6r2B70J06cyMyZM2nbti2nT5+mWbNmdOjQQeq0gNzbjs+fP5/U1FS6dOlC+/btWb9+vdRpCRYiipFQqslkMhISEgB47bXXqFGjBg0aNJA2KYrvQf/GjRtoNBrUajXfffcdnp6eVKpUSeq0AFiyZAl9+vThjz/+oH79+hw+fJjff/9d6rQECxHFSCjVPvzwQ2JiYgCws7NDr9fzySefSJxV8T3ob9myhY0bNwJQvnx5duzYUaxaHz4+Phw5coR27dphZ2dHdna21CkJFiKKkVCqRUREMHHiRCD3F/wTJ04kNDRU4qyK70E/OzvbaFaD4jTDgZubGzNnzuTq1au0atWKuXPn4uXlJXVagoWIod1CqSaTybh16xY1a9YE4N69e5LPvgDF96DfoUMHRowYQZcuXQDYv3+/0XRKUpo+fTpnzpxh+PDh2NraUqFChWJxyw3BMsSPXoVS7eTJk3z88ceGWajj4+OZN28eTZs2lTSv+fPn888//xgd9Bs1asT7778vaV4Ae/fu5dy5cyiVSpo2bVosrmUBdOnShT179kidhvCCiGIklHpZWVncvn0bpVJJ1apVUavVUqcEFN+DfnE1ceJEWrduTf369Y1mqhBddaWDKEZCqVbYfGZiHrOSp127dvlek8lkxeJGiYL5pO88F4QXqFmzZoZ/5+TkcOjQIapWrSphRsLzOnz4sNQpCC+QaBkJ/6/o9XoGDRrEpk2bpE5FKCLRyi3dRMtI+H/l3r17ht8dCSWLaOWWbqIYCaVarVq1kMlk/NsBUKZMGT744AOJsxKeR+/evY2e9+vXj0GDBkmUjWBpohgJpdrNmzelTkF4QUQrt3QRxUgolZYsWfLU5eLHkiWPaOWWbqIYCaXa5cuXiYqKonPnziiVSg4cOIC3t7fUaQnPQbRySzcxmk4o1QYOHMjq1auxsbEBIDMzk+HDh7N582aJMxOKKj09nSVLlnDq1Cm0Wi3NmzdnwoQJ2NraSp2aYAFiolShVIuPj0cmkxmeZ2dnG24pIZQsX375Jenp6Xz11Vd8/fXXZGdn88UXX0idlmAhoptOKNXeeOMN+vbti5+fH3q9nj///JPhw4dLnZbwHK5du8bOnTsNzz///PNicat2wTJEy0go1d555x26devGxo0bsbe3Z8iQIQwZMkTqtITnoNfrSUpKMjxPSkpCoVBImJFgSaJlJJRqCxYsICoqinLlyjFixAjee+89IiIimDRpktSpCUX05ptv8sYbb9C2bVtDK3f06NFSpyVYiGgZCaXaiRMnmD9/PlZWVjg4OLB69WqOHTsmdVrCc+jevTtDhgzBwcGBChUqMGzYsGJxbyrBMsQnKZRqcnnu+da/gxiysrIMrwkly/vvv09sbCw+Pj6Eh4cbXu/Vq5d0SQkWI4qRUKp17tyZ999/n8TERH755Rd27txJt27dpE5LeA7BwcHs3btX6jSEF0QUI6FUGz16NMePH8fLy4vIyEgCAgJo27at1GkJz6FixYpERESIm+mVUuJHr4IgFGvDhg1DJpOh0WiIjIykVq1aRqPo1q5dK2F2gqWIlpEgCMVaQECA1CkI/wHRMhIEQRAkJ4YVCYIgCJITxUgQBEGQnChGgiAIguREMRIEQRAkJ4qRIAiCILn/A6TTkuXdftt5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Correlation matrix between numerical values\n",
+    "g = sns.heatmap(dataset[numeric_features].corr(),annot=True, fmt = \".2f\", cmap = \"coolwarm\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "57867c79-49b3-4e90-a196-aa9a297040a3",
+    "_execution_state": "idle",
+    "_uuid": "56537a5cce38758ab1e8514cbb7e12da27339984"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Education Num vs Income\n",
+    "g = sns.factorplot(x=\"education.num\",y=\"income\",data=dataset,kind=\"bar\",size = 6,palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\">50K probability\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "40c1d5aa-7f1c-4cdd-94c6-fd074d404162",
+    "_execution_state": "idle",
+    "_uuid": "7d13c85496761accb5791803b816e6152d5b4fc8"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Hours Per Week vs Income\n",
+    "g  = sns.factorplot(x=\"hours.per.week\",y=\"income\",data=dataset,kind=\"bar\",size = 6,palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\">50K probability\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "c719cef4-5d7f-4218-b5d5-f6ec2941a51e",
+    "_execution_state": "idle",
+    "_uuid": "2fa3fe2ff1f5dc96f2502767ce87263d86eb077b"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Age vs Income\n",
+    "g = sns.FacetGrid(dataset, col='income')\n",
+    "g = g.map(sns.distplot, \"age\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "cb5fb656-d724-4448-97b5-931560fcf9f2",
+    "_execution_state": "idle",
+    "_uuid": "076ad21bd294eb420d70df41407144cf12903e67"
+   },
+   "source": [
+    "###3.2. Categorical Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "_cell_guid": "dbccf607-7eb6-4e8b-8c00-2e91a2477ffe",
+    "_execution_state": "idle",
+    "_uuid": "0367e18fa3ea49392b03696aa5e75443c98ea471"
+   },
+   "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": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fill Missing Category Entries\n",
+    "dataset[\"workclass\"] = dataset[\"workclass\"].fillna(\"X\")\n",
+    "dataset[\"occupation\"] = dataset[\"occupation\"].fillna(\"X\")\n",
+    "dataset[\"native.country\"] = dataset[\"native.country\"].fillna(\"United-States\")\n",
+    "\n",
+    "# Confirm All Missing Data is Handled\n",
+    "dataset.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "_cell_guid": "5c6b1b5c-886d-44b1-ac09-1d410cda49d1",
+    "_execution_state": "idle",
+    "_uuid": "557e3c876124213d21e797095c23f6e0b9a5c5ab"
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'seaborn' has no attribute 'plt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-53-0138039b851b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"native.country\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"income\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Income >50K Probability\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'plt'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Explore Native Nation vs Income\n",
+    "g = sns.barplot(x=\"native.country\",y=\"income\",data=dataset)\n",
+    "g = g.set_ylabel(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "30b367e2-afec-49fa-b52a-0371f1d6c0bc",
+    "_execution_state": "idle",
+    "_uuid": "36779048741bd7d8c4be010aab17761657dcc952"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Sex vs Income\n",
+    "g = sns.barplot(x=\"sex\",y=\"income\",data=dataset)\n",
+    "g = g.set_ylabel(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "70d38dd5-6ea6-4f6b-a5ea-2cd8d7c5e3b4",
+    "_execution_state": "idle",
+    "_uuid": "2b3ef28f7ccd536387bb5dc29f30a63df5c1f8a8"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Relationship vs Income\n",
+    "g = sns.factorplot(x=\"relationship\",y=\"income\",data=dataset,kind=\"bar\", size = 6 ,\n",
+    "palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "c35908c8-7587-4dd6-826a-044a61a1fddd",
+    "_execution_state": "idle",
+    "_uuid": "8b0379c50391da248a88b6062e290200af00e65b"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Marital Status vs Income\n",
+    "g = sns.factorplot(x=\"marital.status\",y=\"income\",data=dataset,kind=\"bar\", size = 6 ,\n",
+    "palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_cell_guid": "6dcfbb5a-dff6-4d90-9ed5-9c217810a7ea",
+    "_execution_state": "idle",
+    "_uuid": "5199bc6918c701af26e84802b068fc858f9d0617"
+   },
+   "outputs": [],
+   "source": [
+    "# Explore Workclass vs Income\n",
+    "g = sns.factorplot(x=\"workclass\",y=\"income\",data=dataset,kind=\"bar\", size = 6 ,\n",
+    "palette = \"muted\")\n",
+    "g.despine(left=True)\n",
+    "g = g.set_ylabels(\"Income >50K Probability\")\n",
+    "sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "f370f948-2810-48a9-b54f-b4c9c8ff851d",
+    "_execution_state": "idle",
+    "_uuid": "be23c9e7f4907134e69ca32b3e50de52e7f0a111"
+   },
+   "source": [
+    "##4. Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "_cell_guid": "1f2a9e2f-273b-495b-a615-4e9b8dd33c69",
+    "_execution_state": "idle",
+    "_uuid": "33f5e2439162592c594918029c6ececc4d6fa817"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset with Dropped Labels\n",
+      "   age  fnlwgt  education.num  marital.status  sex  capital.gain  \\\n",
+      "0   90   77053              9               0    0             0   \n",
+      "1   82  132870              9               0    0             0   \n",
+      "2   66  186061             10               0    0             0   \n",
+      "3   54  140359              4               0    0             0   \n",
+      "4   41  264663             10               0    0             0   \n",
+      "\n",
+      "   capital.loss  hours.per.week  income  \n",
+      "0          4356              40       0  \n",
+      "1          4356              18       0  \n",
+      "2          4356              40       0  \n",
+      "3          3900              40       0  \n",
+      "4          3900              40       0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "####################################################\n",
+    "############### FEATURE ENGINEERING ################\n",
+    "####################################################\n",
+    "# Convert Sex value to 0 and 1\n",
+    "dataset[\"sex\"] = dataset[\"sex\"].map({\"Male\": 1, \"Female\":0})\n",
+    "\n",
+    "# Create Married Column - Binary Yes(1) or No(0)\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].replace(['Never-married','Divorced','Separated','Widowed'], 'Single')\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].replace(['Married-civ-spouse','Married-spouse-absent','Married-AF-spouse'], 'Married')\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].map({\"Married\":1, \"Single\":0})\n",
+    "dataset[\"marital.status\"] = dataset[\"marital.status\"].astype(int)\n",
+    "\n",
+    "# Drop the data you don't want to use\n",
+    "dataset.drop(labels=[\"workclass\",\"education\",\"occupation\",\"relationship\",\"race\",\"native.country\"], axis = 1, inplace = True)\n",
+    "print('Dataset with Dropped Labels')\n",
+    "print(dataset.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "46f46b2b-9ecc-4010-88c2-ae7872f80d8f",
+    "_execution_state": "idle",
+    "_uuid": "ab574da3a5eed4439cd48fe5dd6e681f01778d6e"
+   },
+   "source": [
+    "##5. Modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "_cell_guid": "2168869f-7cc2-4156-94a2-f1d27faaea17",
+    "_execution_state": "idle",
+    "_uuid": "a8b064a5427aa5e314094466a394d623bf7f020c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Split Data: X\n",
+      "[[90 '?' 77053 ... 'Widowed' '?' 'Not-in-family']\n",
+      " [82 'Private' 132870 ... 'Widowed' 'Exec-managerial' 'Not-in-family']\n",
+      " [66 '?' 186061 ... 'Widowed' '?' 'Unmarried']\n",
+      " ...\n",
+      " [40 'Private' 154374 ... 'Married-civ-spouse' 'Machine-op-inspct'\n",
+      "  'Husband']\n",
+      " [58 'Private' 151910 ... 'Widowed' 'Adm-clerical' 'Unmarried']\n",
+      " [22 'Private' 201490 ... 'Never-married' 'Adm-clerical' 'Own-child']]\n",
+      "Split Data: Y\n",
+      "['White' 'White' 'Black' ... 'White' 'White' 'White']\n",
+      "LR: nan (nan)\n",
+      "LDA: nan (nan)\n",
+      "KNN: nan (nan)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: 'Private'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1342, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: 'Private'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\discriminant_analysis.py\", line 424, in fit\n",
+      "    X, y = self._validate_data(X, y, ensure_min_samples=2, estimator=self,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: 'Private'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\neighbors\\_base.py\", line 1131, in fit\n",
+      "    X, y = self._validate_data(X, y, accept_sparse=\"csr\",\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: 'Private'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CART: nan (nan)\n",
+      "NB: nan (nan)\n",
+      "RF: nan (nan)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 890, in fit\n",
+      "    super().fit(\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\tree\\_classes.py\", line 156, in fit\n",
+      "    X, y = self._validate_data(X, y,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 429, in _validate_data\n",
+      "    X = check_array(X, **check_X_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: 'Private'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\", line 210, in fit\n",
+      "    X, y = self._validate_data(X, y)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: 'Private'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n",
+      "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\model_selection\\_validation.py\", line 531, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\", line 303, in fit\n",
+      "    X, y = self._validate_data(X, y, multi_output=True,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\", line 432, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 795, in check_X_y\n",
+      "    X = check_array(X, accept_sparse=accept_sparse,\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 72, in inner_f\n",
+      "    return f(**kwargs)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\", line 598, in check_array\n",
+      "    array = np.asarray(array, order=order, dtype=dtype)\n",
+      "  File \"C:\\Users\\Usman Gohar\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\", line 83, in asarray\n",
+      "    return array(a, dtype, copy=False, order=order)\n",
+      "ValueError: could not convert string to float: '?'\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "###################################################\n",
+    "##################### MODELING #####################\n",
+    "####################################################\n",
+    "# Split-out Validation Dataset and Create Test Variables\n",
+    "array = dataset.values\n",
+    "X = array[:,0:8]\n",
+    "Y = array[:,8]\n",
+    "print('Split Data: X')\n",
+    "print(X)\n",
+    "print('Split Data: Y')\n",
+    "print(Y)\n",
+    "validation_size = 0.20\n",
+    "seed = 7\n",
+    "num_folds = 10\n",
+    "scoring = 'accuracy'\n",
+    "X_train, X_validation, Y_train, Y_validation = train_test_split(X,Y,\n",
+    "    test_size=validation_size,random_state=seed)\n",
+    "\n",
+    "# Params for Random Forest\n",
+    "num_trees = 100\n",
+    "max_features = 3\n",
+    "\n",
+    "#Spot Check 5 Algorithms (LR, LDA, KNN, CART, GNB, SVM)\n",
+    "models = []\n",
+    "models.append(('LR', LogisticRegression()))\n",
+    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
+    "models.append(('KNN', KNeighborsClassifier()))\n",
+    "models.append(('CART', DecisionTreeClassifier()))\n",
+    "models.append(('NB', GaussianNB()))\n",
+    "models.append(('RF', RandomForestClassifier(n_estimators=num_trees, max_features=max_features)))\n",
+    "#models.append(('SVM', SVC()))\n",
+    "# evalutate each model in turn\n",
+    "results = []\n",
+    "names = []\n",
+    "for name, model in models:\n",
+    "    kfold = KFold(n_splits=10, random_state=seed)\n",
+    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
+    "    results.append(cv_results)\n",
+    "    names.append(name)\n",
+    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "_cell_guid": "28db75e2-b091-4199-a358-e83b26e29836",
+    "_execution_state": "idle",
+    "_uuid": "8be8d86aeb3f97e762f7184db80c2fa4f5fcb5ed"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "fig.suptitle('Algorith Comparison')\n",
+    "ax = fig.add_subplot(111)\n",
+    "plt.boxplot(results)\n",
+    "ax.set_xticklabels(names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "bc5073e0-41df-4105-8215-ad8cb89241f8",
+    "_execution_state": "idle",
+    "_uuid": "a016da59293800e79278556bae8a0aa550e46184"
+   },
+   "source": [
+    "##6. Algorithm Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "_cell_guid": "ce7f4775-1714-482c-a6de-ceeae6fac0d2",
+    "_execution_state": "idle",
+    "_uuid": "6d3b6b7ac9a070d9c9513d56d40aa3e2a75ce997"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nCommented Out to Reduce Script Time - Took 20 Minutes to run.\\nbest n_estimator = 250\\nbest max_feature = 5\\n# Tune Random Forest\\nn_estimators = np.array([50,100,150,200,250])\\nmax_features = np.array([1,2,3,4,5])\\nparam_grid = dict(n_estimators=n_estimators,max_features=max_features)\\nmodel = RandomForestClassifier()\\nkfold = KFold(n_splits=num_folds, random_state=seed)\\ngrid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\\ngrid_result = grid.fit(X_train, Y_train)\\nprint(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\\nmeans = grid_result.cv_results_[\\'mean_test_score\\']\\nstds = grid_result.cv_results_[\\'std_test_score\\']\\nparams = grid_result.cv_results_[\\'params\\']\\nfor mean, stdev, param in zip(means, stds, params):\\n    print(\"%f (%f) with: %r\" % (mean, stdev, param))\\n'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "####################################################\n",
+    "################ ALGORITHM TUNING ##################\n",
+    "####################################################\n",
+    "'''\n",
+    "Commented Out to Reduce Script Time - Took 20 Minutes to run.\n",
+    "best n_estimator = 250\n",
+    "best max_feature = 5\n",
+    "# Tune Random Forest\n",
+    "n_estimators = np.array([50,100,150,200,250])\n",
+    "max_features = np.array([1,2,3,4,5])\n",
+    "param_grid = dict(n_estimators=n_estimators,max_features=max_features)\n",
+    "model = RandomForestClassifier()\n",
+    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
+    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\n",
+    "grid_result = grid.fit(X_train, Y_train)\n",
+    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
+    "means = grid_result.cv_results_['mean_test_score']\n",
+    "stds = grid_result.cv_results_['std_test_score']\n",
+    "params = grid_result.cv_results_['params']\n",
+    "for mean, stdev, param in zip(means, stds, params):\n",
+    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "52cb2e35-1e66-4e7c-bc74-4e7e149d6a60",
+    "_execution_state": "idle",
+    "_uuid": "7491499e7b6de7b0b50cd671a4db9262062d0679"
+   },
+   "source": [
+    "##7. Finalize Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "_cell_guid": "a1c36e3d-3aaa-4952-854a-1d9093387928",
+    "_execution_state": "idle",
+    "_uuid": "6a62aa15b02a8d0de1166725e8122344418785ed"
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: '?'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-22-48629fd47ec8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# a) Predictions on validation dataset - KNN\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mrandom_forest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m250\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mrandom_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_validation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Accuracy: %s%%\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_validation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    301\u001b[0m                 \u001b[1;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m             )\n\u001b[1;32m--> 303\u001b[1;33m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[0m\u001b[0;32m    304\u001b[0m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    596\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
+      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \"\"\"\n\u001b[1;32m---> 83\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '?'"
+     ]
+    }
+   ],
+   "source": [
+    "####################################################\n",
+    "################# FINALIZE MODEL ###################\n",
+    "####################################################\n",
+    "# 5. Finalize Model\n",
+    "# a) Predictions on validation dataset - KNN\n",
+    "random_forest = RandomForestClassifier(n_estimators=250,max_features=5)\n",
+    "random_forest.fit(X_train, Y_train)\n",
+    "predictions = random_forest.predict(X_validation)\n",
+    "print(\"Accuracy: %s%%\" % (100*accuracy_score(Y_validation, predictions)))\n",
+    "print(confusion_matrix(Y_validation, predictions))\n",
+    "print(classification_report(Y_validation, predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fairness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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": 56,
+   "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": 57,
+   "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": 59,
+   "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>marital.status</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital.gain</th>\n",
+       "      <th>capital.loss</th>\n",
+       "      <th>hours.per.week</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>264663</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32556</th>\n",
+       "      <td>22</td>\n",
+       "      <td>310152</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32557</th>\n",
+       "      <td>27</td>\n",
+       "      <td>257302</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32558</th>\n",
+       "      <td>40</td>\n",
+       "      <td>154374</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32559</th>\n",
+       "      <td>58</td>\n",
+       "      <td>151910</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32560</th>\n",
+       "      <td>22</td>\n",
+       "      <td>201490</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>32561 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age  fnlwgt  education.num  marital.status  sex  capital.gain  \\\n",
+       "0       90   77053              9               0    0             0   \n",
+       "1       82  132870              9               0    0             0   \n",
+       "2       66  186061             10               0    0             0   \n",
+       "3       54  140359              4               0    0             0   \n",
+       "4       41  264663             10               0    0             0   \n",
+       "...    ...     ...            ...             ...  ...           ...   \n",
+       "32556   22  310152             10               0    1             0   \n",
+       "32557   27  257302             12               1    0             0   \n",
+       "32558   40  154374              9               1    1             0   \n",
+       "32559   58  151910              9               0    0             0   \n",
+       "32560   22  201490              9               0    1             0   \n",
+       "\n",
+       "       capital.loss  hours.per.week  income  \n",
+       "0              4356              40       0  \n",
+       "1              4356              18       0  \n",
+       "2              4356              40       0  \n",
+       "3              3900              40       0  \n",
+       "4              3900              40       0  \n",
+       "...             ...             ...     ...  \n",
+       "32556             0              40       0  \n",
+       "32557             0              38       0  \n",
+       "32558             0              40       1  \n",
+       "32559             0              40       0  \n",
+       "32560             0              20       0  \n",
+       "\n",
+       "[32561 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "privileged_groups = [{'sex': 1}]\n",
+    "unprivileged_groups = [{'sex': 0}]\n",
+    "dataset_orig = StandardDataset(dataset,\n",
+    "                                  label_name='income',\n",
+    "                                  protected_attribute_names=['sex'],\n",
+    "                                  favorable_classes=[1],\n",
+    "                                  privileged_classes=[[1]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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": 62,
+   "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 = 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": 67,
+   "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": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0         1         2         3         4         5\n",
+      "0     1 -0.200256 -0.127940  0.121698  0.358552  0.141998\n",
+      "1     2 -0.161759 -0.157158  0.118837  0.263874  0.166721\n",
+      "2     3 -0.194439 -0.143516  0.121965  0.344118  0.137786\n",
+      "3     4 -0.166929 -0.137121  0.106883  0.296598  0.150633\n",
+      "4     5 -0.202344 -0.097317  0.105592  0.317806  0.133387\n",
+      "..  ...       ...       ...       ...       ...       ...\n",
+      "95   96 -0.203133 -0.138285  0.121751  0.287236  0.131505\n",
+      "96   97 -0.201736 -0.136655  0.119637  0.292519  0.130226\n",
+      "97   98 -0.204693 -0.157954  0.130652  0.280221  0.131178\n",
+      "98   99 -0.201255 -0.138133  0.119947  0.296843  0.130144\n",
+      "99  100 -0.200221 -0.141715  0.122040  0.291758  0.133016\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>0.825264</td>\n",
+       "      <td>0.794145</td>\n",
+       "      <td>0.808373</td>\n",
+       "      <td>0.808988</td>\n",
+       "      <td>0.820964</td>\n",
+       "      <td>0.816767</td>\n",
+       "      <td>0.824445</td>\n",
+       "      <td>0.822090</td>\n",
+       "      <td>0.822193</td>\n",
+       "      <td>0.825878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.827209</td>\n",
+       "      <td>0.826390</td>\n",
+       "      <td>0.825366</td>\n",
+       "      <td>0.827209</td>\n",
+       "      <td>0.827516</td>\n",
+       "      <td>0.826594</td>\n",
+       "      <td>0.828027</td>\n",
+       "      <td>0.827720</td>\n",
+       "      <td>0.827413</td>\n",
+       "      <td>0.825264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1</th>\n",
+       "      <td>0.621088</td>\n",
+       "      <td>0.580430</td>\n",
+       "      <td>0.533632</td>\n",
+       "      <td>0.599399</td>\n",
+       "      <td>0.580475</td>\n",
+       "      <td>0.613725</td>\n",
+       "      <td>0.598078</td>\n",
+       "      <td>0.622666</td>\n",
+       "      <td>0.601879</td>\n",
+       "      <td>0.627871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.628030</td>\n",
+       "      <td>0.625276</td>\n",
+       "      <td>0.624063</td>\n",
+       "      <td>0.625388</td>\n",
+       "      <td>0.627460</td>\n",
+       "      <td>0.625055</td>\n",
+       "      <td>0.628647</td>\n",
+       "      <td>0.626581</td>\n",
+       "      <td>0.628307</td>\n",
+       "      <td>0.621088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistical_parity_difference</th>\n",
+       "      <td>-0.200221</td>\n",
+       "      <td>-0.200256</td>\n",
+       "      <td>-0.161759</td>\n",
+       "      <td>-0.194439</td>\n",
+       "      <td>-0.166929</td>\n",
+       "      <td>-0.202344</td>\n",
+       "      <td>-0.189774</td>\n",
+       "      <td>-0.203620</td>\n",
+       "      <td>-0.191020</td>\n",
+       "      <td>-0.201049</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.200172</td>\n",
+       "      <td>-0.203430</td>\n",
+       "      <td>-0.203875</td>\n",
+       "      <td>-0.197134</td>\n",
+       "      <td>-0.202046</td>\n",
+       "      <td>-0.203133</td>\n",
+       "      <td>-0.201736</td>\n",
+       "      <td>-0.204693</td>\n",
+       "      <td>-0.201255</td>\n",
+       "      <td>-0.200221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equal_opportunity_difference</th>\n",
+       "      <td>-0.141715</td>\n",
+       "      <td>-0.127940</td>\n",
+       "      <td>-0.157158</td>\n",
+       "      <td>-0.143516</td>\n",
+       "      <td>-0.137121</td>\n",
+       "      <td>-0.097317</td>\n",
+       "      <td>-0.192581</td>\n",
+       "      <td>-0.119619</td>\n",
+       "      <td>-0.128854</td>\n",
+       "      <td>-0.116834</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.138133</td>\n",
+       "      <td>-0.139763</td>\n",
+       "      <td>-0.136977</td>\n",
+       "      <td>-0.133529</td>\n",
+       "      <td>-0.145011</td>\n",
+       "      <td>-0.138285</td>\n",
+       "      <td>-0.136655</td>\n",
+       "      <td>-0.157954</td>\n",
+       "      <td>-0.138133</td>\n",
+       "      <td>-0.141715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_abs_odds_difference</th>\n",
+       "      <td>0.122040</td>\n",
+       "      <td>0.121698</td>\n",
+       "      <td>0.118837</td>\n",
+       "      <td>0.121965</td>\n",
+       "      <td>0.106883</td>\n",
+       "      <td>0.105592</td>\n",
+       "      <td>0.141009</td>\n",
+       "      <td>0.114309</td>\n",
+       "      <td>0.114540</td>\n",
+       "      <td>0.110926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.119313</td>\n",
+       "      <td>0.122473</td>\n",
+       "      <td>0.121728</td>\n",
+       "      <td>0.116094</td>\n",
+       "      <td>0.123466</td>\n",
+       "      <td>0.121751</td>\n",
+       "      <td>0.119637</td>\n",
+       "      <td>0.130652</td>\n",
+       "      <td>0.119947</td>\n",
+       "      <td>0.122040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disparate_impact</th>\n",
+       "      <td>-1.231829</td>\n",
+       "      <td>-1.025681</td>\n",
+       "      <td>-1.332284</td>\n",
+       "      <td>-1.066770</td>\n",
+       "      <td>-1.215378</td>\n",
+       "      <td>-1.146315</td>\n",
+       "      <td>-1.364657</td>\n",
+       "      <td>-1.178312</td>\n",
+       "      <td>-1.274683</td>\n",
+       "      <td>-1.183894</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.203130</td>\n",
+       "      <td>-1.243116</td>\n",
+       "      <td>-1.236700</td>\n",
+       "      <td>-1.202605</td>\n",
+       "      <td>-1.232950</td>\n",
+       "      <td>-1.247452</td>\n",
+       "      <td>-1.229225</td>\n",
+       "      <td>-1.272178</td>\n",
+       "      <td>-1.214553</td>\n",
+       "      <td>-1.231829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theil_index</th>\n",
+       "      <td>0.133016</td>\n",
+       "      <td>0.141998</td>\n",
+       "      <td>0.166721</td>\n",
+       "      <td>0.137786</td>\n",
+       "      <td>0.150633</td>\n",
+       "      <td>0.133387</td>\n",
+       "      <td>0.143870</td>\n",
+       "      <td>0.130869</td>\n",
+       "      <td>0.141372</td>\n",
+       "      <td>0.129717</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.130203</td>\n",
+       "      <td>0.131305</td>\n",
+       "      <td>0.131512</td>\n",
+       "      <td>0.131586</td>\n",
+       "      <td>0.130633</td>\n",
+       "      <td>0.131505</td>\n",
+       "      <td>0.130226</td>\n",
+       "      <td>0.131178</td>\n",
+       "      <td>0.130144</td>\n",
+       "      <td>0.133016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               classifier        T0        T1        T2  \\\n",
+       "accuracy                         0.825264  0.794145  0.808373  0.808988   \n",
+       "f1                               0.621088  0.580430  0.533632  0.599399   \n",
+       "statistical_parity_difference   -0.200221 -0.200256 -0.161759 -0.194439   \n",
+       "equal_opportunity_difference    -0.141715 -0.127940 -0.157158 -0.143516   \n",
+       "average_abs_odds_difference      0.122040  0.121698  0.118837  0.121965   \n",
+       "disparate_impact                -1.231829 -1.025681 -1.332284 -1.066770   \n",
+       "theil_index                      0.133016  0.141998  0.166721  0.137786   \n",
+       "\n",
+       "                                     T3        T4        T5        T6  \\\n",
+       "accuracy                       0.820964  0.816767  0.824445  0.822090   \n",
+       "f1                             0.580475  0.613725  0.598078  0.622666   \n",
+       "statistical_parity_difference -0.166929 -0.202344 -0.189774 -0.203620   \n",
+       "equal_opportunity_difference  -0.137121 -0.097317 -0.192581 -0.119619   \n",
+       "average_abs_odds_difference    0.106883  0.105592  0.141009  0.114309   \n",
+       "disparate_impact              -1.215378 -1.146315 -1.364657 -1.178312   \n",
+       "theil_index                    0.150633  0.133387  0.143870  0.130869   \n",
+       "\n",
+       "                                     T7        T8  ...       T90       T91  \\\n",
+       "accuracy                       0.822193  0.825878  ...  0.827209  0.826390   \n",
+       "f1                             0.601879  0.627871  ...  0.628030  0.625276   \n",
+       "statistical_parity_difference -0.191020 -0.201049  ... -0.200172 -0.203430   \n",
+       "equal_opportunity_difference  -0.128854 -0.116834  ... -0.138133 -0.139763   \n",
+       "average_abs_odds_difference    0.114540  0.110926  ...  0.119313  0.122473   \n",
+       "disparate_impact              -1.274683 -1.183894  ... -1.203130 -1.243116   \n",
+       "theil_index                    0.141372  0.129717  ...  0.130203  0.131305   \n",
+       "\n",
+       "                                    T92       T93       T94       T95  \\\n",
+       "accuracy                       0.825366  0.827209  0.827516  0.826594   \n",
+       "f1                             0.624063  0.625388  0.627460  0.625055   \n",
+       "statistical_parity_difference -0.203875 -0.197134 -0.202046 -0.203133   \n",
+       "equal_opportunity_difference  -0.136977 -0.133529 -0.145011 -0.138285   \n",
+       "average_abs_odds_difference    0.121728  0.116094  0.123466  0.121751   \n",
+       "disparate_impact              -1.236700 -1.202605 -1.232950 -1.247452   \n",
+       "theil_index                    0.131512  0.131586  0.130633  0.131505   \n",
+       "\n",
+       "                                    T96       T97       T98       T99  \n",
+       "accuracy                       0.828027  0.827720  0.827413  0.825264  \n",
+       "f1                             0.628647  0.626581  0.628307  0.621088  \n",
+       "statistical_parity_difference -0.201736 -0.204693 -0.201255 -0.200221  \n",
+       "equal_opportunity_difference  -0.136655 -0.157954 -0.138133 -0.141715  \n",
+       "average_abs_odds_difference    0.119637  0.130652  0.119947  0.122040  \n",
+       "disparate_impact              -1.229225 -1.272178 -1.214553 -1.231829  \n",
+       "theil_index                    0.130226  0.131178  0.130144  0.133016  \n",
+       "\n",
+       "[7 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "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
+}