diff --git "a/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/3-income-prediction-xgbclassifier-auc-0-926-checkpoint.ipynb" "b/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/3-income-prediction-xgbclassifier-auc-0-926-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/3-income-prediction-xgbclassifier-auc-0-926-checkpoint.ipynb"
@@ -0,0 +1,3855 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.050457,
+     "end_time": "2021-03-09T19:39:58.015137",
+     "exception": false,
+     "start_time": "2021-03-09T19:39:57.964680",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Please give it a vote if you like it**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.049048,
+     "end_time": "2021-03-09T19:39:58.113695",
+     "exception": false,
+     "start_time": "2021-03-09T19:39:58.064647",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a></a>\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "\n",
+    "[1. Abstract](#1)\n",
+    "\n",
+    "[2. Division of the data between train and test](#2)\n",
+    "\n",
+    "[3. Data Exploration with target varaible](#3)\n",
+    "\n",
+    "[4. Data pre-processing](#4)\n",
+    "\n",
+    "[5. Baseline model:](#5)\n",
+    "\n",
+    "   -Model with missing values\n",
+    "   \n",
+    "   -Model imputing missing values\n",
+    "   \n",
+    "   -Models with SMOTE and UNDER techniques for imbalanced datasets\n",
+    "\n",
+    "[6. Tuning and testing](#6)\n",
+    "\n",
+    "[7. Shapply values - Feature importance](#7)\n",
+    "\n",
+    "[8. Prediction and Evaluation](#8)\n",
+    "\n",
+    "[9. Conclusion](#9)\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.048978,
+     "end_time": "2021-03-09T19:39:58.211558",
+     "exception": false,
+     "start_time": "2021-03-09T19:39:58.162580",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "# 1.  Abstract \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "This data set was provided by Ronny Kohavi and Barry Becker from the 1994 US Cenus and retrieved from: \"http://archive.ics.uci.edu/ml/datasets/Adul\".\n",
+    "\n",
+    "The task of this project is to classify whether a person makes more or less than 50K per year.\n",
+    "\n",
+    "The dataset is composed of 14 variables (6 continuous and 8 nominal).\n",
+    "    \n",
+    "\n",
+    "Data with missing values: \n",
+    "    - 0 : 76%\n",
+    "    - 1: 24%\n",
+    "    \n",
+    "Data dropping missing values: \n",
+    "    - 0: 0.7510% \n",
+    "    - 1: 0.2589%\n",
+    "\n",
+    "\n",
+    "****Imbalance data set probelm**\n",
+    "**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:39:58.321197Z",
+     "iopub.status.busy": "2021-03-09T19:39:58.320374Z",
+     "iopub.status.idle": "2021-03-09T19:40:02.526180Z",
+     "shell.execute_reply": "2021-03-09T19:40:02.527007Z"
+    },
+    "papermill": {
+     "duration": 4.266665,
+     "end_time": "2021-03-09T19:40:02.527241",
+     "exception": false,
+     "start_time": "2021-03-09T19:39:58.260576",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt \n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "from category_encoders.one_hot import OneHotEncoder\n",
+    "import imblearn \n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
+    "from imblearn.pipeline import Pipeline\n",
+    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold\n",
+    "from sklearn.metrics import classification_report,confusion_matrix,roc_auc_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import RidgeClassifier, LinearRegression\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from numpy import mean\n",
+    "from numpy import std\n",
+    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
+    "from xgboost.sklearn import XGBClassifier\n",
+    "import shap\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.049496,
+     "end_time": "2021-03-09T19:40:02.626651",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:02.577155",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "# 2. Division of the data between train and test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:02.740830Z",
+     "iopub.status.busy": "2021-03-09T19:40:02.740054Z",
+     "iopub.status.idle": "2021-03-09T19:40:02.905359Z",
+     "shell.execute_reply": "2021-03-09T19:40:02.904724Z"
+    },
+    "papermill": {
+     "duration": 0.22887,
+     "end_time": "2021-03-09T19:40:02.905542",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:02.676672",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Full data shape is (32561, 15)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education.num</th>\n",
+       "      <th>marital.status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital.gain</th>\n",
+       "      <th>capital.loss</th>\n",
+       "      <th>hours.per.week</th>\n",
+       "      <th>native.country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>?</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>82</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>132870</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>18</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>?</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4356</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>140359</td>\n",
+       "      <td>7th-8th</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>264663</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Own-child</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
+       "0   90         ?   77053       HS-grad              9        Widowed   \n",
+       "1   82   Private  132870       HS-grad              9        Widowed   \n",
+       "2   66         ?  186061  Some-college             10        Widowed   \n",
+       "3   54   Private  140359       7th-8th              4       Divorced   \n",
+       "4   41   Private  264663  Some-college             10      Separated   \n",
+       "\n",
+       "          occupation   relationship   race     sex  capital.gain  \\\n",
+       "0                  ?  Not-in-family  White  Female             0   \n",
+       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
+       "2                  ?      Unmarried  Black  Female             0   \n",
+       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
+       "4     Prof-specialty      Own-child  White  Female             0   \n",
+       "\n",
+       "   capital.loss  hours.per.week native.country income  \n",
+       "0          4356              40  United-States  <=50K  \n",
+       "1          4356              18  United-States  <=50K  \n",
+       "2          4356              40  United-States  <=50K  \n",
+       "3          3900              40  United-States  <=50K  \n",
+       "4          3900              40  United-States  <=50K  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "income_df = pd.read_csv(\"../input/adult-census-income/adult.csv\")\n",
+    "print('Full data shape is {}'.format(income_df.shape))\n",
+    "income_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:03.017593Z",
+     "iopub.status.busy": "2021-03-09T19:40:03.016507Z",
+     "iopub.status.idle": "2021-03-09T19:40:03.077495Z",
+     "shell.execute_reply": "2021-03-09T19:40:03.078231Z"
+    },
+    "papermill": {
+     "duration": 0.121723,
+     "end_time": "2021-03-09T19:40:03.078402",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:02.956679",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Before dividing the data set between train and test. We will do some quick cleaning\n",
+    "\n",
+    "# # Deleting duplicates \n",
+    "income_df.duplicated().sum()\n",
+    "\n",
+    "#Replacing '?' for nans\n",
+    "income_df = income_df.replace('?', np.NaN)\n",
+    "\n",
+    "\n",
+    "# Mapping already our target variable in  1 and 0.\n",
+    "income_df.income = income_df.income.map({'<=50K':0, '>50K':1}) ## mapping already our target variable in  1 and 0.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:03.185097Z",
+     "iopub.status.busy": "2021-03-09T19:40:03.184439Z",
+     "iopub.status.idle": "2021-03-09T19:40:03.204096Z",
+     "shell.execute_reply": "2021-03-09T19:40:03.203418Z"
+    },
+    "papermill": {
+     "duration": 0.074435,
+     "end_time": "2021-03-09T19:40:03.204221",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:03.129786",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>NaN</td>\n",
+       "      <td>77053</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>186061</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>NaN</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",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>264663</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Own-child</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
+       "0   90       NaN   77053       HS-grad              9        Widowed   \n",
+       "1   82   Private  132870       HS-grad              9        Widowed   \n",
+       "2   66       NaN  186061  Some-college             10        Widowed   \n",
+       "3   54   Private  140359       7th-8th              4       Divorced   \n",
+       "4   41   Private  264663  Some-college             10      Separated   \n",
+       "\n",
+       "          occupation   relationship   race     sex  capital.gain  \\\n",
+       "0                NaN  Not-in-family  White  Female             0   \n",
+       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
+       "2                NaN      Unmarried  Black  Female             0   \n",
+       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
+       "4     Prof-specialty      Own-child  White  Female             0   \n",
+       "\n",
+       "   capital.loss  hours.per.week native.country  income  \n",
+       "0          4356              40  United-States       0  \n",
+       "1          4356              18  United-States       0  \n",
+       "2          4356              40  United-States       0  \n",
+       "3          3900              40  United-States       0  \n",
+       "4          3900              40  United-States       0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "income_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:03.314313Z",
+     "iopub.status.busy": "2021-03-09T19:40:03.313669Z",
+     "iopub.status.idle": "2021-03-09T19:40:03.357113Z",
+     "shell.execute_reply": "2021-03-09T19:40:03.356182Z"
+    },
+    "papermill": {
+     "duration": 0.101489,
+     "end_time": "2021-03-09T19:40:03.357278",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:03.255789",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train data shape is (26048, 15)\n",
+      "Test data shape is (6513, 15)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data, test_data = train_test_split(income_df,\n",
+    "                                         test_size=0.2,\n",
+    "                                         stratify=income_df['income'],\n",
+    "                                         random_state=5)\n",
+    "print('Train data shape is {}'.format(train_data.shape))\n",
+    "print('Test data shape is {}'.format(test_data.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.053184,
+     "end_time": "2021-03-09T19:40:03.464088",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:03.410904",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    " \n",
+    "# 3. Data Exploration with target varaible"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.052642,
+     "end_time": "2021-03-09T19:40:03.569527",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:03.516885",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Correlation Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "_kg_hide-input": false,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:03.706122Z",
+     "iopub.status.busy": "2021-03-09T19:40:03.705422Z",
+     "iopub.status.idle": "2021-03-09T19:40:04.587721Z",
+     "shell.execute_reply": "2021-03-09T19:40:04.588400Z"
+    },
+    "papermill": {
+     "duration": 0.966895,
+     "end_time": "2021-03-09T19:40:04.588562",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:03.621667",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff05d621d0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#In order for LabelEncoder to work we need to drop all nans\n",
+    "corr_data = train_data.dropna()\n",
+    "categorical_data =corr_data.select_dtypes(include=['object'])\n",
+    "numerical_data = corr_data.select_dtypes(exclude=['object'])\n",
+    "encoder = LabelEncoder()\n",
+    "categorical_data = categorical_data.apply(encoder.fit_transform)\n",
+    "### concatenating numerical and categorical data which was enconded\n",
+    "corr_data = pd.concat([numerical_data, categorical_data], axis=1)\n",
+    "\n",
+    "fig = plt.figure(figsize=(15,7))\n",
+    "ax1 = fig.add_subplot(111)\n",
+    "\n",
+    "#Correlation Matrix with  full data\n",
+    "correlations = corr_data.corr()\n",
+    "matrix = np.triu(correlations.corr())\n",
+    "sns.heatmap(correlations, annot= True,  mask=matrix, cmap='coolwarm', ax=ax1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.054747,
+     "end_time": "2021-03-09T19:40:04.698187",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:04.643440",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Correlation with the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:04.824675Z",
+     "iopub.status.busy": "2021-03-09T19:40:04.823480Z",
+     "iopub.status.idle": "2021-03-09T19:40:05.096455Z",
+     "shell.execute_reply": "2021-03-09T19:40:05.097008Z"
+    },
+    "papermill": {
+     "duration": 0.342136,
+     "end_time": "2021-03-09T19:40:05.097176",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:04.755040",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=((7,7)))\n",
+    "ax2 = fig.add_subplot(111)\n",
+    "#Correlation Matix with Target Variable\n",
+    "sns.heatmap(corr_data.corr()[['income']].sort_values('income').tail(10),vmin=0, cmap='Blues', annot=True, ax=ax2)\n",
+    "ax2.invert_yaxis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.055783,
+     "end_time": "2021-03-09T19:40:05.208715",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.152932",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can observe that we do not have highly correlated variables. Only 0.58 sex and relationship, followed by education and education_num 0.35. \n",
+    "\n",
+    "Additionally, we can observe the above correlation matrix graph where we can see the correlation with our target variable.\n",
+    "- Education_num is positively correlated with income (0.34). This is reasonable since the more education you have, you more likely you are to have a higher salary. \n",
+    "- Age is another variable  that is positively correlated with income (0.23). However, this variable is parabolic and can bee seen in the below visualiztion that there is a point in the age where income will start decreasing. \n",
+    "- Hours per week is another variable postively correlated with income (0.23). The more hours you work, the higher your salary will tend to be. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.055371,
+     "end_time": "2021-03-09T19:40:05.319829",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.264458",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Descriptive statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.055313,
+     "end_time": "2021-03-09T19:40:05.430689",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.375376",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "##### For categorical variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:05.576572Z",
+     "iopub.status.busy": "2021-03-09T19:40:05.572466Z",
+     "iopub.status.idle": "2021-03-09T19:40:05.661748Z",
+     "shell.execute_reply": "2021-03-09T19:40:05.661191Z"
+    },
+    "papermill": {
+     "duration": 0.174165,
+     "end_time": "2021-03-09T19:40:05.661870",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.487705",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>workclass</th>\n",
+       "      <th>education</th>\n",
+       "      <th>marital.status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>native.country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>24571</td>\n",
+       "      <td>26048</td>\n",
+       "      <td>26048</td>\n",
+       "      <td>24567</td>\n",
+       "      <td>26048</td>\n",
+       "      <td>26048</td>\n",
+       "      <td>26048</td>\n",
+       "      <td>25580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Private</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>United-States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>18137</td>\n",
+       "      <td>8397</td>\n",
+       "      <td>12046</td>\n",
+       "      <td>3325</td>\n",
+       "      <td>10633</td>\n",
+       "      <td>22221</td>\n",
+       "      <td>17442</td>\n",
+       "      <td>23324</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       workclass education      marital.status      occupation relationship  \\\n",
+       "count      24571     26048               26048           24567        26048   \n",
+       "unique         8        16                   7              14            6   \n",
+       "top      Private   HS-grad  Married-civ-spouse  Prof-specialty      Husband   \n",
+       "freq       18137      8397               12046            3325        10633   \n",
+       "\n",
+       "         race    sex native.country  \n",
+       "count   26048  26048          25580  \n",
+       "unique      5      2             41  \n",
+       "top     White   Male  United-States  \n",
+       "freq    22221  17442          23324  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_cols = ['workclass', 'education', 'marital.status', 'occupation',\n",
+    "       'relationship', 'race', 'sex', 'native.country']\n",
+    "train_data[cat_cols].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.057899,
+     "end_time": "2021-03-09T19:40:05.776408",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.718509",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### For numerical variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:05.898176Z",
+     "iopub.status.busy": "2021-03-09T19:40:05.897495Z",
+     "iopub.status.idle": "2021-03-09T19:40:05.909279Z",
+     "shell.execute_reply": "2021-03-09T19:40:05.908633Z"
+    },
+    "papermill": {
+     "duration": 0.076891,
+     "end_time": "2021-03-09T19:40:05.909522",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.832631",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>Standard_deviation</th>\n",
+       "      <th>Mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>13.687973</td>\n",
+       "      <td>38.617322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education.num</th>\n",
+       "      <td>2.568641</td>\n",
+       "      <td>10.077664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital.gain</th>\n",
+       "      <td>7430.285729</td>\n",
+       "      <td>1085.146153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>capital.loss</th>\n",
+       "      <td>404.700384</td>\n",
+       "      <td>88.124654</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Standard_deviation         Mean\n",
+       "age                     13.687973    38.617322\n",
+       "education.num            2.568641    10.077664\n",
+       "capital.gain          7430.285729  1085.146153\n",
+       "capital.loss           404.700384    88.124654"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_cols =['age', 'education.num', 'capital.gain', 'capital.loss']\n",
+    "Mean =train_data[num_cols].mean()\n",
+    "Standard_deviation = train_data[num_cols].std()\n",
+    "result = pd.DataFrame({'Standard_deviation': Standard_deviation, 'Mean': Mean})\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.056474,
+     "end_time": "2021-03-09T19:40:06.023525",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:05.967051",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can observe that the average age is around 38.6 and education_num is 10.07 (which equals to some college).\n",
+    "\n",
+    "We can see that capital_gain and capital_loss standard_deviation is higher than mean. This shows that people vary greatly in terms of capital gain and loss. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.055999,
+     "end_time": "2021-03-09T19:40:06.137481",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:06.081482",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "## 3.1 Data visualization with target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:06.257622Z",
+     "iopub.status.busy": "2021-03-09T19:40:06.256883Z",
+     "iopub.status.idle": "2021-03-09T19:40:06.262653Z",
+     "shell.execute_reply": "2021-03-09T19:40:06.263152Z"
+    },
+    "papermill": {
+     "duration": 0.069075,
+     "end_time": "2021-03-09T19:40:06.263299",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:06.194224",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.759175\n",
+       "1    0.240825\n",
+       "Name: income, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.income.value_counts()/len(train_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.063301,
+     "end_time": "2021-03-09T19:40:06.390540",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:06.327239",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can observe that our target variable is imbalanced ( <= 50K:76%, > 50k: 24%). Therefore, we will address this problem later with some techniques for imbalanced classification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.056825,
+     "end_time": "2021-03-09T19:40:06.504453",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:06.447628",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Age vs income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "_kg_hide-input": false,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:06.622777Z",
+     "iopub.status.busy": "2021-03-09T19:40:06.622046Z",
+     "iopub.status.idle": "2021-03-09T19:40:06.868419Z",
+     "shell.execute_reply": "2021-03-09T19:40:06.867674Z"
+    },
+    "papermill": {
+     "duration": 0.306897,
+     "end_time": "2021-03-09T19:40:06.868550",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:06.561653",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7eff0464ced0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,4))\n",
+    "ax1 = fig.add_subplot(111)\n",
+    "\n",
+    "data_over50k=train_data[train_data['income']==1]\n",
+    "data_less50k= train_data[train_data['income']==0]\n",
+    "sns.kdeplot(data_less50k['age'], label = '<=50K', shade=True, color='#ADC6DF', ax=ax1)\n",
+    "sns.kdeplot(data_over50k['age'], label = '>50K', shade=True, color='#9B73B5', ax=ax1)\n",
+    "\n",
+    "#Removing lines from the graph\n",
+    "ax1.spines['right'].set_visible(False)\n",
+    "ax1.spines['top'].set_visible(False)\n",
+    "ax1.spines['left'].set_visible(False)\n",
+    "ax1.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "#Title\n",
+    "ax1.set_title(\"Income by Age\", loc='center',fontweight='bold',fontsize=14 )\n",
+    "ax1.set_xlabel(' ')\n",
+    "ax1.set_ylabel(' ')\n",
+    "#X-Axis\n",
+    "plt.xticks(np.arange(10,100,10))\n",
+    "\n",
+    "#Legend\n",
+    "line_labels = [\"<=50K\", \">50K\"]\n",
+    "ax1.legend(\n",
+    "    loc=\"upper right\",\n",
+    "    labels=line_labels)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "_kg_hide-input": false,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:07.006868Z",
+     "iopub.status.busy": "2021-03-09T19:40:07.004060Z",
+     "iopub.status.idle": "2021-03-09T19:40:08.088461Z",
+     "shell.execute_reply": "2021-03-09T19:40:08.087882Z"
+    },
+    "papermill": {
+     "duration": 1.160091,
+     "end_time": "2021-03-09T19:40:08.088585",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:06.928494",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7eff045cab10>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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66roCU19fH4888si61xgYGGD33XcHYIsttmD+/PncdtttfOELXxiy47CQJEmSJEmS1EUnn3wyBx98MA8++CCXXXYZN998M7Nnz2abbbYBWLfuUUqJq666iv333x+AGTNmcNlll5FS4pZbbmGHHXZg0qRJ6153woQJLFiwgG9/+9tDNjPJb20bg6ouB5t++NQh7okkSZIkSUOr/+/fPeRtnnjiiVxyySWMH19ehnn/+9/Pk08+SUqJgw46iG984xsAHHvssVx77bW86U1vYsKECfzjP/7jBrE777wz1113He94xzvYddddmTlzZlePxUKSJEmSJElSF82YMaP2+Ztuuql0e0Twta99rfS5559/ft39yZMns3z58o3vYAcsJGlIORtKkiRJkqTe5RpJkiRJkiRJytLVQlJE7BgRV0bEfRFxb0QcHhE7R8SNEbGsuN2pbf+zI+KBiLg/Io7uZt8kSZIkSdLYkFIa7i6MSBtzXro9I+mrwHUppTcDBwL3AmcBC1NKU4CFxWMiYj9gFjANOAa4ICLGdbl/kiRJkiRpFNtmm21YtWqVxaRBUkqsWrVq3TfH5eraGkkRsT3wDuAUgJTSS8BLETETOKLY7VJgEfBXwExgXkppNbA8Ih4ADgUWd6uPw831giRJkiRJ6q6+vj4GBgZ48sknh7srI84222xDX19fRzHdXGx7H+BJ4B8j4kDgp8AcYLeU0kqAlNLKiJhY7L8HcEtb/ECxbT0R0Q/0A1x44YX09/d37wgkSZIkSVJP23LLLdl7772HuxujRjcLSeOBg4GPpJRujYivUlzGViFKtm0w7yylNBeYW/W8JEmSJEmSuqObayQNAAMppVuLx1fSKiw9HhGTAIrbJ9r2n9wW3wc81sX+SZIkSZIkqQNdm5GUUvpVRDwSEfumlO4HjgTuKX5mA+cWt1cXIdcAl0fEV4DdgSnAkm71TxoqroUlSZIkSRotunlpG8BHgG9HxFbAg8CptGZBzY+I04CHgRMAUkp3R8R8WoWmV4AzUkprutw/SZIkSZIkZepqISmldAdwSMlTR1bsfw5wTjf7JG0MZxVJkiRJktT9GUmSepTFM0mSJEnSYN1cbFuSJEmSJEmjiDOSNkHVjA1w1sZI47+VJEmSJEmbzhlJkiRJkiRJyuKMpB7l+jWSJEmSJGmoOSNJkiRJkiRJWSwkSZIkSZIkKYuXtkkjmJcwSpIkSZJGEmckSZIkSZIkKYuFJEmSJEmSJGXx0jZpFOvFS+Oq+gwju9+SJEmSNBY4I0mSJEmSJElZLCRJkiRJkiQpi4UkSZIkSZIkZbGQJEmSJEmSpCwuti1JhV5cnFySJEmShpKFJEmbnQUZSZIkSRqdvLRNkiRJkiRJWSwkSZIkSZIkKYuFJEmSJEmSJGXpaiEpIlZExC8i4o6IuL3YtnNE3BgRy4rbndr2PzsiHoiI+yPi6G72TZIkSZIkSZ0ZihlJf5RSOiildEjx+CxgYUppCrCweExE7AfMAqYBxwAXRMS4IeifJEmSJEmSMgzHpW0zgUuL+5cCx7Vtn5dSWp1SWg48ABw6DP2TJEmSJElSiW4XkhJwQ0T8NCL6i227pZRWAhS3E4vtewCPtMUOFNskSZIkSZI0Aozv8uu/LaX0WERMBG6MiPtq9o2SbWmDnVoFqX6ACy+8kP7+/g2CJI1dCxYvLd0+/fCpQ9wTSZIkSRp9ulpISik9Vtw+ERHfp3Wp2uMRMSmltDIiJgFPFLsPAJPbwvuAx0pecy4wd+3DrnVekiRJkiRJ6+napW0R8dqI2G7tfeAo4C7gGmB2sdts4Ori/jXArIjYOiL2BqYAS7rVP0mSJEmSJHWmmzOSdgO+HxFr27k8pXRdRNwGzI+I04CHgRMAUkp3R8R84B7gFeCMlNKaLvZPkiRJkiRJHehaISml9CBwYMn2VcCRFTHnAOd0q0+SJEmSJEnaeN1ebFuSxgQX+ZYkSZI0FnRtjSRJkiRJkiSNLhaSJEmSJEmSlMVCkiRJkiRJkrJYSJIkSZIkSVIWC0mSJEmSJEnKYiFJkiRJkiRJWSwkSZIkSZIkKYuFJEmSJEmSJGUZP9wdkCTBgsVLS7dPP3zqEPdEkiRJkqo5I0mSJEmSJElZnJEkST3O2UySJEmShoozkiRJkiRJkpTFQpIkSZIkSZKyjPlL27wkRJIkSZIkKc+YLyRJkoaHhXxJkiSp93hpmyRJkiRJkrJYSJIkSZIkSVIWC0mSJEmSJEnKYiFJkiRJkiRJWSwkSZIkSZIkKUvXv7UtIsYBtwOPppSmR8TOwHeAvYAVwIkppaeLfc8GTgPWAB9NKV3f7f5J0ljWq9+c1qv9liRJknrdUMxImgPc2/b4LGBhSmkKsLB4TETsB8wCpgHHABcURShJkiRJkiSNAF0tJEVEH/AnwEVtm2cClxb3LwWOa9s+L6W0OqW0HHgAOLSb/ZMkSZIkSVK+bl/a9j+ATwHbtW3bLaW0EiCltDIiJhbb9wBuadtvoNi2nojoB/oBLrzwQvr7+7vRb0lSg6rLy8BLzCRJkqTRqmuFpIiYDjyRUvppRByRE1KyLW2wIaW5wNyq5yVJquP6SpIkSdLG6+aMpLcBMyLiWGAbYPuI+BbweERMKmYjTQKeKPYfACa3xfcBj3Wxf5IkSZIkSepA19ZISimdnVLqSyntRWsR7ZtSSicD1wCzi91mA1cX968BZkXE1hGxNzAFWNKt/kmSJEmSJKkz3V4jqcy5wPyIOA14GDgBIKV0d0TMB+4BXgHOSCmtGYb+SZIkSZIkqcSQFJJSSouARcX9VcCRFfudA5wzFH2SJEmSJElSZ7p2aZskSZIkSZJGFwtJkiRJkiRJymIhSZIkSZIkSVksJEmSJEmSJCmLhSRJkiRJkiRlsZAkSZIkSZKkLOOHuwOSJPWSBYuXlm6ffvjUIe6JJEmSNPQsJEmS1AMsYEmSJGkksJAkSdIQsRgkSZKkXucaSZIkSZIkScpiIUmSJEmSJElZLCRJkiRJkiQpi4UkSZIkSZIkZbGQJEmSJEmSpCwWkiRJkiRJkpTFQpIkSZIkSZKyjB/uDmwOCxYvLd0+/fCpQ9wTSZIkSZKk0csZSZIkSZIkScoyKmYkSZKkalUzd8HZu5IkSeqMM5IkSZIkSZKUxUKSJEmSJEmSsnStkBQR20TEkoj4eUTcHRGfL7bvHBE3RsSy4nantpizI+KBiLg/Io7uVt8kSZIkSZLUuW7OSFoNvDOldCBwEHBMRBwGnAUsTClNARYWj4mI/YBZwDTgGOCCiBjXxf5JkiRJkiSpA10rJKWW54uHWxY/CZgJXFpsvxQ4rrg/E5iXUlqdUloOPAAc2q3+SZIkSZIkqTNdXSMpIsZFxB3AE8CNKaVbgd1SSisBituJxe57AI+0hQ8U2wa/Zn9E3B4Rt8+dO7eb3ZckSZIkSVKb8d188ZTSGuCgiNgR+H5E7F+ze5S9RMlrzgXmVj0vSZJGjgWLl5Zun3741CHuiSRJkjaHIfnWtpTSM8AiWmsfPR4RkwCK2yeK3QaAyW1hfcBjQ9E/SZIkSZIkNevmt7a9rpiJRES8BviPwH3ANcDsYrfZwNXF/WuAWRGxdUTsDUwBlnSrf5IkSZIkSepMNy9tmwRcWnzz2hbA/JTSgohYDMyPiNOAh4ETAFJKd0fEfOAe4BXgjOLSOEmSpI55WZ0kSdLm17VCUkrpTuB3S7avAo6siDkHOKdbfZIkSZIkSdLGG5I1kiRJkiRJktT7LCRJkiRJkiQpi4UkSZIkSZIkZbGQJEmSJEmSpCwWkiRJkiRJkpSla9/aJkmSRocFi5eWbp9++NRR2a4kSZKqOSNJkiRJkiRJWSwkSZIkSZIkKYuFJEmSJEmSJGWxkCRJkiRJkqQsFpIkSZIkSZKUxUKSJEmSJEmSsowf7g5IkqSNN/evv1e6vf/v3z3EPZEkSdJYYCFJkqQxzEKUJEmSOmEhSZIkaZAFi5eWbp9++NQh7okkSdLI4hpJkiRJkiRJymIhSZIkSZIkSVm8tE2SRrBNWb9muGJ7VdUxg+dMkiRJWstCkiRpRBmLBZmxeMybYlOLft3m+kqSJGk0s5AkSRo1LMhorLOIJUmSus1CkiRps7OgI0mSJI1OXSskRcRk4DLg9cBvgbkppa9GxM7Ad4C9gBXAiSmlp4uYs4HTgDXAR1NK13erf5I0FEb6JTiSJEmS1IlufmvbK8AnUkpvAQ4DzoiI/YCzgIUppSnAwuIxxXOzgGnAMcAFETGui/2TJEmSJElSB7pWSEoprUwp/ay4/xxwL7AHMBO4tNjtUuC44v5MYF5KaXVKaTnwAHBot/onSZIkSZKkzgzJGkkRsRfwu8CtwG4ppZXQKjZFxMRitz2AW9rCBoptg1+rH+gHuPDCC+nv7+9exyVJ0qjjGl6SJEkbr+uFpIjYFvgu8LGU0q8jonLXkm1pgw0pzQXmVj0vSZLUq6q+dQ1G9zev+W1zkiT1jq4WkiJiS1pFpG+nlNb++e/xiJhUzEaaBDxRbB8AJreF9wGPdbN/ksYOZyBIUj2LOZIkKUfX1kiK1tSji4F7U0pfaXvqGmB2cX82cHXb9lkRsXVE7A1MAZZ0q3+SJEmSJEnqTDdnJL0N+ADwi4i4o9j2X4FzgfkRcRrwMHACQErp7oiYD9xD6xvfzkgpreli/yRJktTjnEklSdLQ6lohKaX0E8rXPQI4siLmHOCcbvVJkiRJm5/FHEmSxo6uXdomSZIkSZKk0aXr39omSZIkjUTOpJIkqXMWkiSpgd/4JpXz/4YkSdLYYyFJkiQNOYtQkiRJvclCkiRJkrQRvDROkjQWudi2JEmSJEmSslhIkiRJkiRJUhYvbZMkSZKGmJfFSZJ6lYUkSUPGxXUl9bpNGcccAyVJ0mhgIUlSR4bzFyF/CZMkSZKk4eUaSZIkSZIkScpiIUmSJEmSJElZvLRNkiRJ6iEu1C1JGk4WkiRJknqA68RJkqSRwEKSNAb5y4gkSZIkaWNYSJIkSZLGCC+LkyRtKhfbliRJkiRJUhYLSZIkSZIkScpiIUmSJEmSJElZLCRJkiRJkiQpi4ttS5IkjXJ+W6ckSdpculZIiohvAtOBJ1JK+xfbdga+A+wFrABOTCk9XTx3NnAasAb4aErp+m71TZIkSaObxTNJkrqjm5e2XQIcM2jbWcDClNIUYGHxmIjYD5gFTCtiLoiIcV3smyRJkiRJkjrUtRlJKaWbI2KvQZtnAkcU9y8FFgF/VWyfl1JaDSyPiAeAQ4HF3eqf1Muq/soK/qVVkiR1z4LFS0u3Tz986hD3RJI0XIZ6jaTdUkorAVJKKyNiYrF9D+CWtv0Gim0biIh+oB/gwgsvpL+/v4vdlSRJGtu8REySJLUbKYttR8m2VLZjSmkuMLduH0mSJA2/TZ1BaxFLkqSRp5trJJV5PCImARS3TxTbB4DJbfv1AY8Ncd8kSZIkSZJUY6hnJF0DzAbOLW6vbtt+eUR8BdgdmAIsGeK+SUPKv7JKkqSxxPWVJGl06FohKSKuoLWw9q4RMQB8llYBaX5EnAY8DJwAkFK6OyLmA/cArwBnpJTWdKtvkiRJUrf4x6LNzyKUJI0c3fzWtvdVPHVkxf7nAOd0qz+SJEmSJEnaNCNlsW1JkiRpRHBGkSRJ1YZ6sW1JkiRJkiT1KGckSZIkSRrVNmWNJddnkqT1OSNJkiRJkiRJWSwkSZIkSZIkKYuFJEmSJEmSJGVxjSRpE/itLpIkSZKkscQZSZIkSZIkScrijCRJkiRJ6gK/8U3SaGQhSZIkSRohvGxea1UVocBClKThZSFJY54f2CRJ0mjgZxptLs6kklTHQpJ6nh+aJEmSpPVZDJLULRaSJEmSpDGu6g9z4B/nJEnrs5AkSZIkSdosNnUmlDOppJHPQpIkSZKkTeJSA5I0cm3uAq2FJEmSJEnDZlOKUBaw1M7ZTNLQ2GK4OyBJkiRJkqTe4IwkjQj+NUmSJEmSpJHPQpIkSZKkMWm4/pjpH1FHHi+Lk/JZSJIkSZKkDlkM0lpVRSiwEKXRyUKS1ql6MwTfECVJkqSRwiLW6LIps6GGK1Zj24grJEXEMcBXgXHARSmlc4e5S8rkG5okSZI0siC8MvcAABFfSURBVHk5X+8Y6efMQtTYNaIKSRExDvga8MfAAHBbRFyTUrpneHsmSZIkSWPbcBY2RnpRRUPHAtbms7H/r0ZUIQk4FHggpfQgQETMA2YCPVdIstIvSZIkScNvU35H2tTfr3rx98Kh6HNVMeixH9zV9bZ70YgrnqWURswP8F5al7OtffwB4PxB+/QDtxc//Q2vV/t8t2KHs22PuXfa9ph7p22PuXfa9ph7p22PuXfa9ph7p22PuXfaHov9HovH3Kv9HovH3Kv9Hq5j3ugGu/EDnMCGhaT/tQmvd/twxA5n2x5z77TtMfdO2x5z77TtMfdO2x5z77TtMfdO2x5z77Q9Fvs9Fo+5V/s9Fo+5V/s9XMe8BSPLADC57XEf8Ngw9UWSJEmSJEltRloh6TZgSkTsHRFbAbOAa4a5T5IkSZIkSWKELbadUnolIv4zcD0wDvhmSunuTXjJucMUO5xte8y907bH3Dtte8y907bH3Dtte8y907bH3Dtte8y90/ZY7PdYPObhbNtj7p22e+6Yo7guTpIkSZIkSao10i5tkyRJkiRJ0ghlIUmSJEmSJEl5NuWr4kbSD/BN4AngrrZt3wHuKH5WAHd0EHsQcEsReztwaAexBwKLgV8A/wRsXxE7GfghcC9wNzCn2L4zcCOwrLjdqYPYE4rHvwUOqTlfVfFfAu4D7gS+D+zYQezfFXF3ADcAu+fGtj3/SSABu3bY788Bj7b9ex/bSdvAR4D7i+3/rYN2c3OsKr4xz2piG/MM2AZYAvy8iP18BzlWFZubY1XxOTlWFZuTY6WxHeRYVds5OVbZdkaOVbXbmGM1sbnjWFV81lhW7DsO+DdgQW6O1cRm5VhFbGN+NcQ35lhVbG6OVbTbmF917TblV0PbWeNYRWxWjlXEdpJfK4r97qD4mtrcHKuIzR3HymKzc6wiPivHymJzc6yi3U5yrLTtnDyraDv3vbIsNnccK4vtJMd2BK4s/m3vBQ7vIMfKYnNzrCy2kxwri8/NsQ1iO8ixsnazcqyq3Zz8qmk7N8fKYnNzrCw29zP/vm39uwP4NfCxnByriW3MsZrYrByric/5PFYam5NjNe025lhduzk5VtN2zuexqticz/tVsbk5dmZxXHcBV9D6bNfJZ7Gy+NxxrCw2N8fKYnPHsA1ic8ewmrYbc6yu7cwcK2u3k89iZfG549icIu5uXv1/kZ0n671Wzk698AO8AziYtoLOoOf/O/CZ3Ngiad9V3D8WWNRB7G3AHxb3PwT8XUXsJODg4v52wFJgP+C/AWcV288Czusg9i20BqJF1P+Hr4o/ChhfbD+vw7a3b9vno8A3cmOLx5NpLbT+ENUfXKra/hzwyYYcqYr9I+BfgK2L5yZ20u/MHKtquzHPamIb8wwIYNvi/pbArcBhmTlWFZubY1XxOTlWFZuTY6WxHeRYVds5OVYVm5Njlf1uyrGadnPHsar4rLGseP7jwOW8WiRozLGa2Kwcq4htzK+G+MYcq4rNzbGKdhvzqya2Mb+a+t2UYzVtZ+VYRWwn+bVi8PnMzbGK2NxxrCw2O8cq4rNyrCw2N8cq2u0kx8ris/Ksqt85OVbRbu44VhbbSY5dCny4uL8VraJBbo6VxebmWFlsJzlWFp+bYxvEdpBjZe1m5VhFbPY4VtXvzBwrazs3x8pis3Os7XXGAb8C3pCbYxWx2e+VJbEdvVeWxGe/Vw6Ozc2xinazcqwitqP3yrJ+5+RYRdvZ75UlsTmf9/cAlgOvKR7PB07Jza+a+MYcq4nN+bxfFZvzeb80Nje/atpuzLGa2JzP+5X9zsmvmrZzfqfcn1YRaQKtL137F2BKbp4M/hk1l7allG4Gnip7LiICOJFWxS43NgHbF/d3AB7rIHZf4Obi/o3AeypiV6aUflbcf47WXzf2AGbSerOiuD0uNzaldG9K6f6y9jLjb0gpvVLsdgvQ10Hsr9t2ey2tc5h7zAD/AHyqLC4zfqOOGTgdODeltLp47olO283Isar4xjyriW3Ms9TyfPFwy+InkZdjpbEd5FhVfE6OVcXm5FjVMUNejtXF16qJzcmx2nbrcqwmNnccq4rPGssiog/4E+Cits2NOVYVm5tjFbGN+dUQ35hjVbGFxhyriW1UEduYXzltN41jFbFZOVYRm5VfNbJyrExujlXEZudYRXxWjtVozLEuyc6zKk05ViErxyrkjmHb0/rD4MUAKaWXUkrPkJFjVbE5OVYTm5VjNfGNOVZzzNCQYw2xtWpis/Krqe26HKuJbcyxmtiNGceOBH6ZUnqIzsexdbEbMY61x27MONYe3+k41n7M0Nk4Nji2E+2xGzOGbdB2B+NYe2yn41h7bG6OjQdeExHjaRUKHqOz/NogvoMcK4vNzbGy2Nz8KjtmyM+vqvgcZbG5OVbZbmZ+lcXn5NhbgFtSSi8U/zY/Ao5nYz9PpcyKbi/8AHtRMiOJ1sB/eyexxYl+GHiE1vS2N3QQ+6/AzOL+x4HnMvv+cJEAzwx67unc2LZti8j460RVfLH9n4CTO4kFzinO2V3A6zo45hnAV4vtK2j460RJ/OeKuDtpXW5YOyVvUOwdwOdpzcL4EfDWjTjfjTlW0XZ2npXEZuUZrb9q3AE8T1Fhzs2xsthOcqwuvinHqmJzcqzimLNzrCI+K8cqYrNyrOF81+ZYRbudjGNl8bk5diXwe8ARvDrbJDfHNojNzbG62Kb8qovPzLGyY87KsYrY3Pwqi80ewxrOd1OOlbWdlWMVsdnvlbT++vYz4KdAf4c5tkFsBzlWGZuZY6XxmTlWdsy5OVYWm5VjNfG541jd+W7KsbJ2c3OsLDZ3DDuI1uW9l9C6/PIiWr+8NOZYVWxOjjXFNuVYXXxTjtUcc2OO1cQ25lhNbG5+NZ3vyhyrabsxx2piN+Yz/zeB/1zc7/Qz/7rYnBxrim3Ksbr4phyrOeaOPvMPim3MsZrYjj7v15zvrM/8g9ru9PN+e2zuODaH1me4J4Fvd5pfZfG5OVYX25RjVbE5+VVxzJ183i+Lz8qxitjccazuXOfULcrazhnH3kLrypZdaBWgFgP/q5M8WW+/nJ165YfqQtLXgU90Egv8T+A9xf0TgX/pIPbNtKaX/RT4LLCqoe1ti33fXTzu5D/9erFt22v/w2fEf5rW9azRaWzx3NkMWpumKrZI5FuBHYrnav/TV5yz3Wj9IrwFrYHnmx3E3lX8ewdwKK0Po6XHXXO+GnOsou1O8mxwbKd5tiOttZb273TAaI/tNMdq4htzrCo2J8cGxR7QaY6VnLPsHCuJzc6xmvOVm2Pt7WbnV0V8Y44B04ELivtH0EEhqSo2J8cyYmvzqym+LsfKYskcx2rOV2N+1cRm5VfGOavMsZq2G3OsJjZ7DKNYHwGYSGstr3fk5FhVbE6OZcTmvE9WxtflWM0xZ41jFbGdvE+WxefmWd05qx3HKtrNGscqYrNyDDgEeAX4D8Xjr9JaoyNnHCuNzcmxjNimcaw2vi7HKmK/lJNjNecrZxyris3Nr6ZzVjeOVbWdM45VxXb6WWwr4N+B3YrHnXzmXy82J8cyYnM/i5XG1+VYWSwdfuYvOV+djGODYzv9LFZ1znJ+rxzcdief9wfH5nwW2wm4CXgdrVnlVwEn5+ZXVXxOjmXEVuZYU2xdflXEfjA3v2rOWc44VhXbmGMZ56vpfbKq7dz3ytNo/dHlZuAbtGZvWUiipJBEa+rX40BfJ7HAs2v/4Ytk+HUn7bY9NxVYUhO7Ja1rOD/etu1+YFJxfxJwf25s23OLaH5TKY0HZtOqUE7oNLbt+TfUnJP1YoHfobVg+Yri5xVaFdXXb2Tbdf8eZef7OuCItse/pPyvd1XnKzfHytrOyrOMY67Ns7b9Pktr8bmsHCuL7STHquJzcqyu7aYcK4n9m05yLKPtyhyrON9ZOVZzvrJyrKTd7HEs45hLcwz4IjBQnNdfAS8A38rJsarYnByri83Jr6a263KsIva7OTmW2W5pftWc69wxrO6c1eZYTduNOZZ5zFljWLHv59j4cexzbOQ41h6bk2NNbdflWEXsRo1jFe2W5ljD+d6Ycaz9nHU6jq1tt+NxrOKYK3MMeD2wou3x24Ef5ORYVWxOjtXF5uRYU9t1OVYRuzAnxzLbLc2xmnOdO47VnbOmcayq7ZxxLOeYG8cxWpeQ3ND2OHscGxybk2N1sTk51tR2XY6VxdL5Z/66dktzrOZcdzSGVZyz3M/8g9vu5PfKumOu+ix2AnBx2+MPAhfk5ldVfE6O1cU25VhTu3X5VRH7w9z8ymy7NMdqzndjjjWcr8b8qml7Y94rvwD8ZW6eDP4ZNWsk1fiPwH0ppYEO4x4D/rC4/05aq5hniYiJxe0WwF/TqvaV7Re0rrW+N6X0lbanrqH1H4/i9uoOYnP7WBofEccAfwXMSCm90GHslLbdZtBaqb8xNqX0i5TSxJTSXimlvWj9wnFwSulXHbQ9qW2342lVhLNiaVVy31nsM5VX/xKQEwsZOVYT35hnNcfcmGcR8bqI2LG4/5q1fSUvx6pis1TFZ+ZYVWxOjpXF/lsHOVbVdk6OVZ2znByrO9+1OVYTmzWO1RxzY46llM5OKfUV53UWcFNK6WQycqwmtlFVbE5+NcQ35lhF7Htycqym3cb8qjlfjfnVEA8NOVYT25hjNcec+1752ojYbu19Wgt43kXeOFYV26gqNjfHauJzxrGy2Ntycqym3cYcq4snbxyrO99N41hVbM77ZNUxZ+VYcQ4fiYh9i01HAveQN45VxTaqiu1gHKuKzxnHymJ/ljmOVbWbM45Vna/ccazufDeNY1WxOeNY1TFn5Vib97H+2ieNOVYT24n1YnNzrCa+McfKYjv5zF/RbtY4VhZLZo7VxEP+75WDYzv5vXLwMefk2MPAYRExofi94Uhaa6rm5ldVfI7S2Mwcq4rNya+y2O91kF9VbefkWNX5ysmxunOdk19V8bmf+dfm0560rgy6gs7GoVflVJt64ac4CSuBl2klzWnF9kuAv+g0FvgDWlMIf05ritzvdRA7h9b1h0uBc6mekvwHtBbGWvv1hnfQWmV9F1p/FVpW3O7cQezxRT9W06poXt9h2w/QurZy7bayVfKrYr9L6z/bnbSuhd0jN3bQPiuonoZY1fb/ofW1mHfS+s8wqYPYrWj9Zf0uWlP93tlJvzNzrKrtxjyriW3MM1qXdP1bEXsXxTcAZOZYVWxujlXF5+RYVWxOjpXGdpBjVW3n5FhVbE6OVfa7Kcdq2s0dx6ris8ayttc5glcvW2rMsZrYrByriG3Mr4b4xhyris3NsYp2G/OrJrYxv5r63ZRjNW1n5VhFbO575T7F6/+c1tfVfjo3x2piG3OsJjYrx2ric8ax0ticHKtpNyvHauJzxrHKfjflWE27Oe+TVbHZYxitNXBuL87PVbQuH8gaxypic98ry2Kzx7GK+KxxrCw2J8dq2s3NsbLY7HGsqt9NOVbTdu57ZVlsJzk2AVhFcdlN7jhWE5ubY2WxneRYWXxujm0Q20GOlbWbm2NlsZ3kWGm/M3OsrO3cHCuLzX2v/DytgstdxXnaOje/auJzc6wsNve9siw2N782iM3Nr5q2c3OsLDYrx6r6nZNfNW3n5tiPaRXQfw4cWWzr6DP72p+1058kSZIkSZKkWmPh0jZJkiRJkiRtBhaSJEmSJEmSlMVCkiRJkiRJkrJYSJIkSZIkSVIWC0mSJEmSJEnKYiFJkiRJkiRJWSwkSZIkSZIkKYuFJEmSJEmSJGWxkCRJkiRJkqQsFpIkSZIkSZKUxUKSJEmSJEmSslhIkiRJkiRJUhYLSZIkSZIkScpiIUmSJEmSJElZLCRJkiRJkiQpi4UkSZIkSZIkZbGQJEmSJEmSpCwWkiRJkiRJkpTFQpIkSZIkSZKyWEiSJEmSJElSFgtJkiRJkiRJyvL/ATey47l1sNl8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1440x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,4))\n",
+    "ax1 = fig.add_subplot(111)\n",
+    "sns.countplot(x='age', hue='income', data= train_data, palette=['#ADC6DF','#9B73B5'])\n",
+    "#Removing lines from the graph\n",
+    "ax1.spines['right'].set_visible(False)\n",
+    "ax1.spines['top'].set_visible(False)\n",
+    "ax1.spines['left'].set_visible(False)\n",
+    "ax1.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "#Title\n",
+    "ax1.set_title(\"Income by Age\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax1.set_xlabel(\" \")\n",
+    "ax1.set_ylabel(' ')\n",
+    "\n",
+    "#Legend\n",
+    "line_labels = [\"<=50K\", \">50K\"]\n",
+    "ax1.legend(\n",
+    "    loc=\"upper right\",\n",
+    "    labels=line_labels)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.06011,
+     "end_time": "2021-03-09T19:40:08.209788",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:08.149678",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can see that younger people are more likely to earn less than 50K per year. Which is obvious since those people do not have any work experience. The rates of people earning more than 50K start to increase as people age into their 30s. \n",
+    "\n",
+    "This is also seen in the purple line(>50K). The line starts to increase from 20  to 45 years old and then slowly decreasing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.060798,
+     "end_time": "2021-03-09T19:40:08.330735",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:08.269937",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Workclass and occupation vs Income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:08.476193Z",
+     "iopub.status.busy": "2021-03-09T19:40:08.465737Z",
+     "iopub.status.idle": "2021-03-09T19:40:09.769332Z",
+     "shell.execute_reply": "2021-03-09T19:40:09.768791Z"
+    },
+    "papermill": {
+     "duration": 1.378278,
+     "end_time": "2021-03-09T19:40:09.769451",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:08.391173",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7eff04226050>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(15,15))\n",
+    "ax1= fig.add_subplot(411)\n",
+    "ax2= fig.add_subplot(412)\n",
+    "ax3= fig.add_subplot(413)\n",
+    "ax4= fig.add_subplot(414)\n",
+    "\n",
+    "data_workclass = round(pd.crosstab(train_data.workclass, train_data.income).div(pd.crosstab(train_data.workclass, train_data.income).apply(sum,1),0),2)\n",
+    "data_occupation = round(pd.crosstab(train_data.occupation, train_data.income).div(pd.crosstab(train_data.occupation, train_data.income).apply(sum,1),0),2)\n",
+    "\n",
+    "## Setting space between both subplots\n",
+    "plt.subplots_adjust(left=None,\n",
+    "                    bottom=None, \n",
+    "                    right=None, \n",
+    "                    top=1, \n",
+    "                    wspace=None, \n",
+    "                    hspace=0.5)\n",
+    "\n",
+    "## Grapphing\n",
+    "sns.countplot(x='workclass', hue='income', data= train_data, ax=ax1, palette=['#ADC6DF','#9B73B5'])\n",
+    "data_workclass.plot.bar(color=['#ADC6DF','#9B73B5'], ax=ax2, edgecolor='w',linewidth=1.3)\n",
+    "\n",
+    "sns.countplot(x='occupation', hue='income', data= train_data, ax=ax3, palette=['#ADC6DF','#9B73B5'])\n",
+    "data_occupation.plot.bar(color=['#ADC6DF','#9B73B5'], ax=ax4, edgecolor='w',linewidth=1.3 )\n",
+    "\n",
+    "## Removing lines from the graph\n",
+    "ax1.spines['right'].set_visible(False)\n",
+    "ax1.spines['top'].set_visible(False)\n",
+    "ax1.spines['left'].set_visible(False)\n",
+    "ax1.spines['bottom'].set_visible(False)\n",
+    "ax2.spines['right'].set_visible(False)\n",
+    "ax2.spines['top'].set_visible(False)\n",
+    "ax2.spines['left'].set_visible(False)\n",
+    "ax2.spines['bottom'].set_visible(False)\n",
+    "ax3.spines['right'].set_visible(False)\n",
+    "ax3.spines['top'].set_visible(False)\n",
+    "ax3.spines['left'].set_visible(False)\n",
+    "ax3.spines['bottom'].set_visible(False)\n",
+    "ax4.spines['right'].set_visible(False)\n",
+    "ax4.spines['top'].set_visible(False)\n",
+    "ax4.spines['left'].set_visible(False)\n",
+    "ax4.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "\n",
+    "## Removing subplots legends\n",
+    "ax1.get_legend().remove()\n",
+    "ax2.get_legend().remove()\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=30, ha='right')\n",
+    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=30, ha='right')\n",
+    "ax3.set_xticklabels(ax3.get_xticklabels(), rotation=30, ha='right')\n",
+    "ax4.set_xticklabels(ax4.get_xticklabels(), rotation=30, ha='right')\n",
+    "ax3.get_legend().remove()\n",
+    "ax4.get_legend().remove()\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Title\n",
+    "ax1.set_title(\"Workclass\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax3.set_title(\"Occupation\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax2.set_title(\"Ratio of Workclass\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax4.set_title(\"Ratio of Occupation\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax1.set_xlabel(\" \")\n",
+    "ax1.set_ylabel(' ')\n",
+    "ax2.set_xlabel(\" \")\n",
+    "ax2.set_ylabel(' ')\n",
+    "ax3.set_xlabel(\" \")\n",
+    "ax3.set_ylabel(' ')\n",
+    "ax4.set_xlabel(\" \")\n",
+    "ax4.set_ylabel(' ')\n",
+    "\n",
+    "\n",
+    "## Legend\n",
+    "line_labels = [\"<=50K\", \">50K\"]\n",
+    "fig.legend(\n",
+    "    loc=\"upper right\",\n",
+    "    labels=line_labels) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.063156,
+     "end_time": "2021-03-09T19:40:09.896704",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:09.833548",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Here we can see interesting things about our data:\n",
+    "\n",
+    "\n",
+    "**Workclass**: \n",
+    "    From our data Private is the largest category by number.  If we look at the ratio, self-empl-inc has almost the same amount of people who earn more than 50K and less than 50K, and is the only category where more people earn above 50K. \n",
+    "    \n",
+    "**Occupation**:\n",
+    "\n",
+    "   We can see that Adm-Clerical and Machine-op-Inspect are the most frequent jobs. In terms of income, we can say that Exec-Managerical has the highest rate of >50K, followed by Armed Forces.  The job with the lowest >50K is Priv-house-serv. \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.063271,
+     "end_time": "2021-03-09T19:40:10.023949",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:09.960678",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Education vs Income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:10.160520Z",
+     "iopub.status.busy": "2021-03-09T19:40:10.159813Z",
+     "iopub.status.idle": "2021-03-09T19:40:10.856832Z",
+     "shell.execute_reply": "2021-03-09T19:40:10.857334Z"
+    },
+    "papermill": {
+     "duration": 0.769678,
+     "end_time": "2021-03-09T19:40:10.857500",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:10.087822",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7eff040256d0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x540 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(15,7.5))\n",
+    "ax1= fig.add_subplot(211)\n",
+    "ax2= fig.add_subplot(212)\n",
+    "data_education = round(pd.crosstab(train_data.education, train_data.income).div(pd.crosstab(train_data.education, train_data.income).apply(sum,1),0),2)\n",
+    "## Setting space between both subplots\n",
+    "plt.subplots_adjust(left=None,\n",
+    "                    bottom=None, \n",
+    "                    right=None, \n",
+    "                    top=1, \n",
+    "                    wspace=None, \n",
+    "                    hspace=0.5)\n",
+    "\n",
+    "## Grapphing\n",
+    "sns.countplot(x='education', hue='income', data= train_data, ax=ax1, palette=['#ADC6DF','#9B73B5'],order=train_data.education.value_counts().index)\n",
+    "data_education.sort_values(by=[0], ascending=False).plot.bar(color=['#ADC6DF','#9B73B5'], ax=ax2, edgecolor='w',linewidth=1.3)\n",
+    "\n",
+    "## Removing lines from the graph\n",
+    "ax1.spines['right'].set_visible(False)\n",
+    "ax1.spines['top'].set_visible(False)\n",
+    "ax1.spines['left'].set_visible(False)\n",
+    "ax1.spines['bottom'].set_visible(False)\n",
+    "ax2.spines['right'].set_visible(False)\n",
+    "ax2.spines['top'].set_visible(False)\n",
+    "ax2.spines['left'].set_visible(False)\n",
+    "ax2.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "## Removing subplots legends\n",
+    "ax1.get_legend().remove()\n",
+    "ax2.get_legend().remove()\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=30, ha='right')\n",
+    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=30, ha='right')\n",
+    "\n",
+    "## Title\n",
+    "ax1.set_title(\"Education\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax2.set_title(\"Ratio of Education\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax1.set_xlabel(\" \")\n",
+    "ax1.set_ylabel(' ')\n",
+    "ax2.set_xlabel(\" \")\n",
+    "ax2.set_ylabel(' ')\n",
+    "\n",
+    "## Legend\n",
+    "line_labels = [\"<=50K\", \">50K\"]\n",
+    "fig.legend(\n",
+    "    loc=\"upper right\",\n",
+    "    labels=line_labels) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.06596,
+     "end_time": "2021-03-09T19:40:10.990489",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:10.924529",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Here we can see that HS-grad is the most frequent education followed by Some college. \n",
+    "Additionally, we can see in the second graph, ratio of education, how the number of <=50 tends to decrease as people tend to have higher education. \n",
+    "\n",
+    "For example:  Doctorate. It seems that just 0.20 people who have doctorate tend to have <=50, the 0.80 tend to have more that 50K per year, which makes sense.  We observe similar ratios with prof-school and Masters.\n",
+    "\n",
+    "Therefore, as we have seen before, Education_num has a huge impact in the income variable. This variable has the stronges correlation with our target variable: 0.34.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.066449,
+     "end_time": "2021-03-09T19:40:11.122587",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:11.056138",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Race vs Income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:11.258897Z",
+     "iopub.status.busy": "2021-03-09T19:40:11.258169Z",
+     "iopub.status.idle": "2021-03-09T19:40:11.675642Z",
+     "shell.execute_reply": "2021-03-09T19:40:11.676305Z"
+    },
+    "papermill": {
+     "duration": 0.487707,
+     "end_time": "2021-03-09T19:40:11.676486",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:11.188779",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7efeff64aa90>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,10))\n",
+    "ax1= fig.add_subplot(221)\n",
+    "ax2= fig.add_subplot(222)\n",
+    "\n",
+    "data_race = round(pd.crosstab(train_data.race, train_data.income).div(pd.crosstab(train_data.race, train_data.income).apply(sum,1),0),2)\n",
+    "\n",
+    "## Grapphing\n",
+    "sns.countplot(x='race', hue='income', data= train_data, ax=ax1, palette=['#ADC6DF','#9B73B5'])\n",
+    "data_race.sort_values(by=[0], ascending=False).plot.bar(color=['#ADC6DF','#9B73B5'], ax=ax2, edgecolor='w',linewidth=1.3 )\n",
+    "\n",
+    "#Removing lines from the graph\n",
+    "ax1.spines['right'].set_visible(False)\n",
+    "ax1.spines['top'].set_visible(False)\n",
+    "ax1.spines['left'].set_visible(False)\n",
+    "ax1.spines['bottom'].set_visible(False)\n",
+    "ax2.spines['right'].set_visible(False)\n",
+    "ax2.spines['top'].set_visible(False)\n",
+    "ax2.spines['left'].set_visible(False)\n",
+    "ax2.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=40, ha='right')\n",
+    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=40, ha='right')\n",
+    "\n",
+    "## Title\n",
+    "ax1.set_title(\"Race\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax2.set_title(\"Ratio of Race\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax1.set_xlabel(\" \")\n",
+    "ax1.set_ylabel(' ')\n",
+    "ax2.set_xlabel(\" \")\n",
+    "ax2.set_ylabel(' ')\n",
+    "\n",
+    "#Removing subplots legends\n",
+    "ax1.get_legend().remove()\n",
+    "ax2.get_legend().remove()\n",
+    "\n",
+    "#Legend\n",
+    "line_labels = [\"<=50K\", \">50K\"]\n",
+    "fig.legend(\n",
+    "    loc=\"upper right\",\n",
+    "    labels=line_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.068481,
+     "end_time": "2021-03-09T19:40:11.812407",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:11.743926",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can see that our sample is monstly White followed by Black race. One interesting thing we can see is that Asian-Pac-Islander have the highest ratio of >50K, followed by White, although they are very similar in terms of the ratio betwee our two classes. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.067997,
+     "end_time": "2021-03-09T19:40:11.947934",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:11.879937",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Gender vs Income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:12.086654Z",
+     "iopub.status.busy": "2021-03-09T19:40:12.085971Z",
+     "iopub.status.idle": "2021-03-09T19:40:12.433466Z",
+     "shell.execute_reply": "2021-03-09T19:40:12.434106Z"
+    },
+    "papermill": {
+     "duration": 0.418465,
+     "end_time": "2021-03-09T19:40:12.434289",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:12.015824",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7efeff57c590>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,9))\n",
+    "ax1= fig.add_subplot(221)\n",
+    "ax2= fig.add_subplot(222)\n",
+    "\n",
+    "data_gender = round(pd.crosstab(train_data.sex, train_data.income).div(pd.crosstab(train_data.sex, train_data.income).apply(sum,1),0),2)\n",
+    "\n",
+    "## Graphing\n",
+    "sns.countplot(x='sex', hue='income', data= train_data, ax=ax1, palette=['#ADC6DF','#9B73B5'])\n",
+    "data_gender.sort_values(by=[0], ascending=False).plot.bar(color=['#ADC6DF','#9B73B5'], ax=ax2, edgecolor='w',linewidth=1.3 )\n",
+    "\n",
+    "#Removing lines from the graph\n",
+    "ax1.spines['right'].set_visible(False)\n",
+    "ax1.spines['top'].set_visible(False)\n",
+    "ax1.spines['left'].set_visible(False)\n",
+    "ax1.spines['bottom'].set_visible(False)\n",
+    "ax2.spines['right'].set_visible(False)\n",
+    "ax2.spines['top'].set_visible(False)\n",
+    "ax2.spines['left'].set_visible(False)\n",
+    "ax2.spines['bottom'].set_visible(False)\n",
+    "\n",
+    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=40, ha='right')\n",
+    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation=40, ha='right')\n",
+    "\n",
+    "#Removing subplots legends\n",
+    "ax1.get_legend().remove()\n",
+    "ax2.get_legend().remove()\n",
+    "\n",
+    "\n",
+    "## Title\n",
+    "ax1.set_title(\"Gender\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax2.set_title(\"Ratio of Gender\", loc='center',fontweight='bold',fontsize=14)\n",
+    "ax1.set_xlabel(\" \")\n",
+    "ax1.set_ylabel(' ')\n",
+    "ax2.set_xlabel(\" \")\n",
+    "ax2.set_ylabel(' ')\n",
+    "\n",
+    "#Legend\n",
+    "line_labels = [\"<=50K\", \">50K\"]\n",
+    "fig.legend(\n",
+    "    loc=\"upper right\",\n",
+    "    labels=line_labels)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.068655,
+     "end_time": "2021-03-09T19:40:12.572249",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:12.503594",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can observe that male gender is the most frequent and also we observe that there are some disproportions distribuitions regarding income.\n",
+    "Close to 10% of females earn more that 50.000 dollars compared to Male gender, where almost 40 percent earn more than 50.000 dollars.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.068742,
+     "end_time": "2021-03-09T19:40:12.710177",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:12.641435",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# 4. Data pre-processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.068268,
+     "end_time": "2021-03-09T19:40:12.848525",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:12.780257",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Dropping duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:12.989126Z",
+     "iopub.status.busy": "2021-03-09T19:40:12.988502Z",
+     "iopub.status.idle": "2021-03-09T19:40:13.057709Z",
+     "shell.execute_reply": "2021-03-09T19:40:13.058180Z"
+    },
+    "papermill": {
+     "duration": 0.14131,
+     "end_time": "2021-03-09T19:40:13.058343",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:12.917033",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.drop_duplicates(inplace=True)\n",
+    "train_data.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:13.215769Z",
+     "iopub.status.busy": "2021-03-09T19:40:13.214884Z",
+     "iopub.status.idle": "2021-03-09T19:40:13.263541Z",
+     "shell.execute_reply": "2021-03-09T19:40:13.264537Z"
+    },
+    "papermill": {
+     "duration": 0.136569,
+     "end_time": "2021-03-09T19:40:13.264717",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:13.128148",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.111499,
+     "end_time": "2021-03-09T19:40:13.468691",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:13.357192",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "####  ? Dropping missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:13.637379Z",
+     "iopub.status.busy": "2021-03-09T19:40:13.636667Z",
+     "iopub.status.idle": "2021-03-09T19:40:13.671732Z",
+     "shell.execute_reply": "2021-03-09T19:40:13.671048Z"
+    },
+    "papermill": {
+     "duration": 0.113396,
+     "end_time": "2021-03-09T19:40:13.671843",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:13.558447",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1315536777415018"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.isnull().mean().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.070933,
+     "end_time": "2021-03-09T19:40:13.812650",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:13.741717",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We are missing 13% of our data. We will create a baseline and compare if dropping these instances will drastically change our model performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.069867,
+     "end_time": "2021-03-09T19:40:13.952853",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:13.882986",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Dropping unncessary columns "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.075864,
+     "end_time": "2021-03-09T19:40:14.099999",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:14.024135",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We will drop the following columns\n",
+    "\n",
+    "- Final weight: this column represents the number of people the census believes the entry represents. Therefore, we will drop this column since it does not give any meaningful information to our model.\n",
+    "- Education: We already have an Education_num column which presents this information as an ordinal variable. We will drop it to avoid redundant information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:14.251357Z",
+     "iopub.status.busy": "2021-03-09T19:40:14.250672Z",
+     "iopub.status.idle": "2021-03-09T19:40:14.256268Z",
+     "shell.execute_reply": "2021-03-09T19:40:14.254903Z"
+    },
+    "papermill": {
+     "duration": 0.085859,
+     "end_time": "2021-03-09T19:40:14.256421",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:14.170562",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cols_to_drop = ['fnlwgt', 'education']\n",
+    "train_data.drop(cols_to_drop, inplace=True, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.07007,
+     "end_time": "2021-03-09T19:40:14.396916",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:14.326846",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5. Baseline model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.069482,
+     "end_time": "2021-03-09T19:40:14.536248",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:14.466766",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5.1 Baseline model dropping missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:14.679198Z",
+     "iopub.status.busy": "2021-03-09T19:40:14.678444Z",
+     "iopub.status.idle": "2021-03-09T19:40:25.510629Z",
+     "shell.execute_reply": "2021-03-09T19:40:25.511168Z"
+    },
+    "papermill": {
+     "duration": 10.905264,
+     "end_time": "2021-03-09T19:40:25.511308",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:14.606044",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest AUC imputing missing values: 0.896\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('model',RandomForestClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'Random Forest AUC imputing missing values: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:25.658059Z",
+     "iopub.status.busy": "2021-03-09T19:40:25.657066Z",
+     "iopub.status.idle": "2021-03-09T19:40:44.211578Z",
+     "shell.execute_reply": "2021-03-09T19:40:44.210731Z"
+    },
+    "papermill": {
+     "duration": 18.628959,
+     "end_time": "2021-03-09T19:40:44.211703",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:25.582744",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBClassifier AUC dropping missing values: 0.926\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data_without_na = train_data.dropna()\n",
+    "\n",
+    "X = train_data_without_na.drop('income', axis=1)\n",
+    "y = train_data_without_na['income']\n",
+    "#Transformin categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('model',XGBClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "print(f'XGBClassifier AUC dropping missing values: {round(mean(scores),3)}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:44.382284Z",
+     "iopub.status.busy": "2021-03-09T19:40:44.381565Z",
+     "iopub.status.idle": "2021-03-09T19:40:55.063164Z",
+     "shell.execute_reply": "2021-03-09T19:40:55.063789Z"
+    },
+    "papermill": {
+     "duration": 10.781086,
+     "end_time": "2021-03-09T19:40:55.063944",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:44.282858",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient Boosting AUC dropping missing values: 0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data_without_na = train_data.dropna()\n",
+    "\n",
+    "X = train_data_without_na.drop('income', axis=1)\n",
+    "y = train_data_without_na['income']\n",
+    "#Transformin categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('model',GradientBoostingClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=-1)\n",
+    "print(f'Gradient Boosting AUC dropping missing values: {round(mean(scores),3)}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:55.237816Z",
+     "iopub.status.busy": "2021-03-09T19:40:55.236736Z",
+     "iopub.status.idle": "2021-03-09T19:40:57.844351Z",
+     "shell.execute_reply": "2021-03-09T19:40:57.844930Z"
+    },
+    "papermill": {
+     "duration": 2.708936,
+     "end_time": "2021-03-09T19:40:57.845075",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:55.136139",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic regression AUC dropping missing values: 0.864\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data_without_na = train_data.dropna()\n",
+    "\n",
+    "X = train_data_without_na.drop('income', axis=1)\n",
+    "y = train_data_without_na['income']\n",
+    "#Transformin categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('model',LogisticRegression())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "print(f'Logistic regression AUC dropping missing values: {round(mean(scores),3)}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.072962,
+     "end_time": "2021-03-09T19:40:57.990118",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:57.917156",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5.2 Baseline imputing mising values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.072033,
+     "end_time": "2021-03-09T19:40:58.135791",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:58.063758",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Since our missing values are categorical variables:\n",
+    "- workclass  \n",
+    "- occupation    \n",
+    "- native_country      \n",
+    "\n",
+    "We will use simple imputer with strategy ='most_frequent' (mode)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:40:58.288330Z",
+     "iopub.status.busy": "2021-03-09T19:40:58.287540Z",
+     "iopub.status.idle": "2021-03-09T19:41:07.108668Z",
+     "shell.execute_reply": "2021-03-09T19:41:07.107959Z"
+    },
+    "papermill": {
+     "duration": 8.90129,
+     "end_time": "2021-03-09T19:41:07.108793",
+     "exception": false,
+     "start_time": "2021-03-09T19:40:58.207503",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest AUC imputing missing values: 0.895\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('model',RandomForestClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'Random Forest AUC imputing missing values: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:41:07.266605Z",
+     "iopub.status.busy": "2021-03-09T19:41:07.263637Z",
+     "iopub.status.idle": "2021-03-09T19:41:27.292696Z",
+     "shell.execute_reply": "2021-03-09T19:41:27.293177Z"
+    },
+    "papermill": {
+     "duration": 20.112038,
+     "end_time": "2021-03-09T19:41:27.293344",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:07.181306",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBClassifier AUC imputing missing values: 0.928\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('model',XGBClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'XGBClassifier AUC imputing missing values: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:41:27.449775Z",
+     "iopub.status.busy": "2021-03-09T19:41:27.449060Z",
+     "iopub.status.idle": "2021-03-09T19:41:39.040107Z",
+     "shell.execute_reply": "2021-03-09T19:41:39.039267Z"
+    },
+    "papermill": {
+     "duration": 11.673784,
+     "end_time": "2021-03-09T19:41:39.040244",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:27.366460",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient Boosting  AUC imputing missing values: 0.922\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('model',GradientBoostingClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'Gradient Boosting  AUC imputing missing values: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:41:39.197891Z",
+     "iopub.status.busy": "2021-03-09T19:41:39.196817Z",
+     "iopub.status.idle": "2021-03-09T19:41:41.703750Z",
+     "shell.execute_reply": "2021-03-09T19:41:41.702778Z"
+    },
+    "papermill": {
+     "duration": 2.59022,
+     "end_time": "2021-03-09T19:41:41.703912",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:39.113692",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic Regression AUC imputing missing values: 0.876\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('model',LogisticRegression())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5, shuffle=True)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'Logistic Regression AUC imputing missing values: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.074704,
+     "end_time": "2021-03-09T19:41:41.853784",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:41.779080",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5.2 Models handling imbalanced data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.073761,
+     "end_time": "2021-03-09T19:41:42.001726",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:41.927965",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Another problem we have encounter was our data was imbalanced: \n",
+    "       - 0    0.76%\n",
+    "       - 1    0.24%\n",
+    "Having an imbalanced data set can create model performance with classification problems. Our class is not very severe therefore we will still try to use: \n",
+    "\n",
+    "\n",
+    "- SMOTE (oversampling): creating more samples for the minority class.\n",
+    "- UNDER : decreasing the majority class.\n",
+    "\n",
+    "In both cases we can see that the models using SMOTE techinique the models performed worse than in UNDER technique. However, still, our data set was not that severly imbalanced and we can see that our model performed better without using any technique. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.073403,
+     "end_time": "2021-03-09T19:41:42.149080",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:42.075677",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### 5.2.1 Using Ovesampling technique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:41:42.308546Z",
+     "iopub.status.busy": "2021-03-09T19:41:42.307449Z",
+     "iopub.status.idle": "2021-03-09T19:42:13.663907Z",
+     "shell.execute_reply": "2021-03-09T19:42:13.664481Z"
+    },
+    "papermill": {
+     "duration": 31.441633,
+     "end_time": "2021-03-09T19:42:13.664627",
+     "exception": false,
+     "start_time": "2021-03-09T19:41:42.222994",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBClassifier AUC imputing missing values + SMOTE: 0.924\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('over', SMOTE()),('model',XGBClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'XGBClassifier AUC imputing missing values + SMOTE: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:42:13.824566Z",
+     "iopub.status.busy": "2021-03-09T19:42:13.823570Z",
+     "iopub.status.idle": "2021-03-09T19:42:43.501796Z",
+     "shell.execute_reply": "2021-03-09T19:42:43.500857Z"
+    },
+    "papermill": {
+     "duration": 29.76281,
+     "end_time": "2021-03-09T19:42:43.501951",
+     "exception": false,
+     "start_time": "2021-03-09T19:42:13.739141",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient Boosting AUC imputing missing values + SMOTE: 0.915\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('over', SMOTE()),('model',GradientBoostingClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'Gradient Boosting AUC imputing missing values + SMOTE: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.07503,
+     "end_time": "2021-03-09T19:42:43.652548",
+     "exception": false,
+     "start_time": "2021-03-09T19:42:43.577518",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### 5.2.2 Using undersample  technique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:42:43.815511Z",
+     "iopub.status.busy": "2021-03-09T19:42:43.812592Z",
+     "iopub.status.idle": "2021-03-09T19:42:53.343557Z",
+     "shell.execute_reply": "2021-03-09T19:42:53.342917Z"
+    },
+    "papermill": {
+     "duration": 9.615502,
+     "end_time": "2021-03-09T19:42:53.343696",
+     "exception": false,
+     "start_time": "2021-03-09T19:42:43.728194",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBClassifier AUC imputing missing values + UNDER: 0.924\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('under', RandomUnderSampler(random_state=5)),('model',XGBClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'XGBClassifier AUC imputing missing values + UNDER: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:42:53.504943Z",
+     "iopub.status.busy": "2021-03-09T19:42:53.503214Z",
+     "iopub.status.idle": "2021-03-09T19:43:00.509837Z",
+     "shell.execute_reply": "2021-03-09T19:43:00.510490Z"
+    },
+    "papermill": {
+     "duration": 7.091202,
+     "end_time": "2021-03-09T19:43:00.510640",
+     "exception": false,
+     "start_time": "2021-03-09T19:42:53.419438",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient Boosting AUC imputing missing values + UNDER: 0.921\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),('under', RandomUnderSampler(random_state=5)),('model',GradientBoostingClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5)\n",
+    "\n",
+    "scores = cross_val_score(pipeline, X, y, scoring='roc_auc', cv=stratified_kfold, n_jobs=3)\n",
+    "\n",
+    "print(f'Gradient Boosting AUC imputing missing values + UNDER: {round(mean(scores),3)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.076905,
+     "end_time": "2021-03-09T19:43:00.663705",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:00.586800",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "#### Summary:\n",
+    "\n",
+    "- Imputing missing values did helped us to increase  AUC in the models. However, the increase it is not huge.\n",
+    "\n",
+    "- Using SMOTE and UNDER techniques did note helped our model to get better results\n",
+    "\n",
+    "- Best model: XGBClassifier AUC imputing missing values: 0.928"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.077664,
+     "end_time": "2021-03-09T19:43:00.819236",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:00.741572",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# 6. Tuning and testing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.077431,
+     "end_time": "2021-03-09T19:43:00.975819",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:00.898388",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "While tuning our best parameters were as follows:\n",
+    "\n",
+    "**The best Parameter for XGBClassifier were: {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 500}**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:43:01.142971Z",
+     "iopub.status.busy": "2021-03-09T19:43:01.141362Z",
+     "iopub.status.idle": "2021-03-09T19:43:01.152548Z",
+     "shell.execute_reply": "2021-03-09T19:43:01.151863Z"
+    },
+    "papermill": {
+     "duration": 0.099259,
+     "end_time": "2021-03-09T19:43:01.152691",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:01.053432",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X = train_data.drop('income', axis=1)\n",
+    "y = train_data['income']\n",
+    "\n",
+    "#Transforming categorical columns\n",
+    "categorical_columns= X.select_dtypes(object).columns\n",
+    "# Using pipeline\n",
+    "steps = [('encoding', OneHotEncoder(cols=categorical_columns)),\n",
+    "         ('imputer',SimpleImputer(missing_values=np.NaN, strategy='most_frequent')),\n",
+    "         ('model',XGBClassifier())]\n",
+    "pipeline = Pipeline(steps=steps)\n",
+    "stratified_kfold = StratifiedKFold(n_splits = 5, random_state=5)\n",
+    "\n",
+    "param_grid = {\n",
+    "    'model__n_estimators': [500],\n",
+    "    'model__learning_rate': [0.1],\n",
+    "    'model__max_depth': [3]\n",
+    "\n",
+    "}\n",
+    "\n",
+    "\n",
+    "grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=stratified_kfold.split(X, y),\n",
+    "                    scoring='roc_auc',  n_jobs=5, verbose=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:43:01.313514Z",
+     "iopub.status.busy": "2021-03-09T19:43:01.310769Z",
+     "iopub.status.idle": "2021-03-09T19:43:57.700933Z",
+     "shell.execute_reply": "2021-03-09T19:43:57.701628Z"
+    },
+    "papermill": {
+     "duration": 56.470136,
+     "end_time": "2021-03-09T19:43:57.701812",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:01.231676",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
+      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   43.0s remaining:  1.1min\n",
+      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   44.8s finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=<generator object _BaseKFold.split at 0x7efeff59dcd0>,\n",
+       "             estimator=Pipeline(steps=[('encoding',\n",
+       "                                        OneHotEncoder(cols=Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',\n",
+       "       'sex', 'native.country'],\n",
+       "      dtype='object'))),\n",
+       "                                       ('imputer',\n",
+       "                                        SimpleImputer(strategy='most_frequent')),\n",
+       "                                       ('model',\n",
+       "                                        XGBClassifier(base_score=None,\n",
+       "                                                      booster=None,\n",
+       "                                                      colsample_...\n",
+       "                                                      monotone_constraints=None,\n",
+       "                                                      n_estimators=100,\n",
+       "                                                      n_jobs=None,\n",
+       "                                                      num_parallel_tree=None,\n",
+       "                                                      random_state=None,\n",
+       "                                                      reg_alpha=None,\n",
+       "                                                      reg_lambda=None,\n",
+       "                                                      scale_pos_weight=None,\n",
+       "                                                      subsample=None,\n",
+       "                                                      tree_method=None,\n",
+       "                                                      validate_parameters=None,\n",
+       "                                                      verbosity=None))]),\n",
+       "             n_jobs=5,\n",
+       "             param_grid={'model__learning_rate': [0.1], 'model__max_depth': [3],\n",
+       "                         'model__n_estimators': [500]},\n",
+       "             scoring='roc_auc', verbose=True)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.fit(X,y) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:43:57.886866Z",
+     "iopub.status.busy": "2021-03-09T19:43:57.886145Z",
+     "iopub.status.idle": "2021-03-09T19:43:57.889626Z",
+     "shell.execute_reply": "2021-03-09T19:43:57.890402Z"
+    },
+    "papermill": {
+     "duration": 0.088474,
+     "end_time": "2021-03-09T19:43:57.890592",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:57.802118",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBClassifier AUC: 0.9296\n",
+      "The best Parameter for XGBClassifier is: {'model__learning_rate': 0.1, 'model__max_depth': 3, 'model__n_estimators': 500}\n"
+     ]
+    }
+   ],
+   "source": [
+    "AUC = grid.best_score_\n",
+    "best_param = grid.best_params_\n",
+    "print(f'XGBClassifier AUC: {round(AUC,4)}')\n",
+    "print(f'The best Parameter for XGBClassifier is: {best_param}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:43:58.054670Z",
+     "iopub.status.busy": "2021-03-09T19:43:58.054001Z",
+     "iopub.status.idle": "2021-03-09T19:43:58.084947Z",
+     "shell.execute_reply": "2021-03-09T19:43:58.085497Z"
+    },
+    "papermill": {
+     "duration": 0.113479,
+     "end_time": "2021-03-09T19:43:58.085645",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:57.972166",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('encoding',\n",
+       "                 OneHotEncoder(cols=Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',\n",
+       "       'sex', 'native.country'],\n",
+       "      dtype='object'))),\n",
+       "                ('imputer', SimpleImputer(strategy='most_frequent')),\n",
+       "                ('model',\n",
+       "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
+       "                               colsample_bylevel=1, colsample_bynode=1,\n",
+       "                               colsample_bytree=1, gamma=0, gpu_id=-1,\n",
+       "                               importance_type='gain',\n",
+       "                               interaction_constraints='', learning_rate=0.1,\n",
+       "                               max_delta_step=0, max_depth=3,\n",
+       "                               min_child_weight=1, missing=nan,\n",
+       "                               monotone_constraints='()', n_estimators=500,\n",
+       "                               n_jobs=0, num_parallel_tree=1, random_state=0,\n",
+       "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
+       "                               subsample=1, tree_method='exact',\n",
+       "                               validate_parameters=1, verbosity=None))])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_model = grid.best_estimator_\n",
+    "full_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.079055,
+     "end_time": "2021-03-09T19:43:58.243346",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:58.164291",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 7. Shapply values - Feature importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:43:58.404100Z",
+     "iopub.status.busy": "2021-03-09T19:43:58.403428Z",
+     "iopub.status.idle": "2021-03-09T19:43:58.531905Z",
+     "shell.execute_reply": "2021-03-09T19:43:58.532472Z"
+    },
+    "papermill": {
+     "duration": 0.210722,
+     "end_time": "2021-03-09T19:43:58.532630",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:58.321908",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th>native.country_33</th>\n",
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+       "      <th>native.country_37</th>\n",
+       "      <th>native.country_38</th>\n",
+       "      <th>native.country_39</th>\n",
+       "      <th>native.country_40</th>\n",
+       "      <th>native.country_41</th>\n",
+       "      <th>native.country_42</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12022</th>\n",
+       "      <td>59</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6210</th>\n",
+       "      <td>23</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7294</th>\n",
+       "      <td>36</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12308</th>\n",
+       "      <td>33</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3424</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 91 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age  workclass_1  workclass_2  workclass_3  workclass_4  workclass_5  \\\n",
+       "12022   59            1            0            0            0            0   \n",
+       "6210    23            0            1            0            0            0   \n",
+       "7294    36            0            1            0            0            0   \n",
+       "12308   33            0            1            0            0            0   \n",
+       "3424    38            0            1            0            0            0   \n",
+       "\n",
+       "       workclass_6  workclass_7  workclass_8  workclass_9  ...  \\\n",
+       "12022            0            0            0            0  ...   \n",
+       "6210             0            0            0            0  ...   \n",
+       "7294             0            0            0            0  ...   \n",
+       "12308            0            0            0            0  ...   \n",
+       "3424             0            0            0            0  ...   \n",
+       "\n",
+       "       native.country_33  native.country_34  native.country_35  \\\n",
+       "12022                  0                  0                  0   \n",
+       "6210                   0                  0                  0   \n",
+       "7294                   0                  0                  0   \n",
+       "12308                  0                  0                  0   \n",
+       "3424                   0                  0                  0   \n",
+       "\n",
+       "       native.country_36  native.country_37  native.country_38  \\\n",
+       "12022                  0                  0                  0   \n",
+       "6210                   0                  0                  0   \n",
+       "7294                   0                  0                  0   \n",
+       "12308                  0                  0                  0   \n",
+       "3424                   0                  0                  0   \n",
+       "\n",
+       "       native.country_39  native.country_40  native.country_41  \\\n",
+       "12022                  0                  0                  0   \n",
+       "6210                   0                  0                  0   \n",
+       "7294                   0                  0                  0   \n",
+       "12308                  0                  0                  0   \n",
+       "3424                   0                  0                  0   \n",
+       "\n",
+       "       native.country_42  \n",
+       "12022                  0  \n",
+       "6210                   0  \n",
+       "7294                   0  \n",
+       "12308                  0  \n",
+       "3424                   0  \n",
+       "\n",
+       "[5 rows x 91 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded_variables = full_model['encoding'].transform(X)\n",
+    "encoded_variables.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:43:58.694830Z",
+     "iopub.status.busy": "2021-03-09T19:43:58.694193Z",
+     "iopub.status.idle": "2021-03-09T19:44:10.251913Z",
+     "shell.execute_reply": "2021-03-09T19:44:10.252570Z"
+    },
+    "papermill": {
+     "duration": 11.640613,
+     "end_time": "2021-03-09T19:44:10.252726",
+     "exception": false,
+     "start_time": "2021-03-09T19:43:58.612113",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x684 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "explainer = shap.TreeExplainer(full_model['model'])\n",
+    "\n",
+    "shap_values = explainer.shap_values(encoded_variables)\n",
+    "shap.summary_plot(shap_values, encoded_variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:10.428616Z",
+     "iopub.status.busy": "2021-03-09T19:44:10.427927Z",
+     "iopub.status.idle": "2021-03-09T19:44:10.685568Z",
+     "shell.execute_reply": "2021-03-09T19:44:10.684948Z"
+    },
+    "papermill": {
+     "duration": 0.344,
+     "end_time": "2021-03-09T19:44:10.685681",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:10.341681",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x684 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shap.summary_plot(shap_values, encoded_variables, plot_type='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.083035,
+     "end_time": "2021-03-09T19:44:10.852095",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:10.769060",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can see that:\n",
+    "- Marital_status_1 (which is Married-civ-spouse) impacts the result of our model on average of 1.0. This means that married civilians have higher probability to have income higher that 50K. This agrees with our Data exploration section where we mentioned that Married-civ-spouse tend to be more likely to have >50K income.\n",
+    "- The second one is age. As explained above, the older one is, the more experience one has and therefore likely to have a higher salary.\n",
+    "- Capital gain is the third most important feature and we deduct that having better access to capital is a key factor in improving income.\n",
+    "Lastly, education is the fourth most important feature. Again, this is logical, as the more education you have, you more likely you are to have a higher salary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.083309,
+     "end_time": "2021-03-09T19:44:11.020555",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:10.937246",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# 8. Prediction and Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:11.193563Z",
+     "iopub.status.busy": "2021-03-09T19:44:11.192560Z",
+     "iopub.status.idle": "2021-03-09T19:44:11.196771Z",
+     "shell.execute_reply": "2021-03-09T19:44:11.197237Z"
+    },
+    "papermill": {
+     "duration": 0.092939,
+     "end_time": "2021-03-09T19:44:11.197401",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:11.104462",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6513, 15)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:11.372781Z",
+     "iopub.status.busy": "2021-03-09T19:44:11.371693Z",
+     "iopub.status.idle": "2021-03-09T19:44:11.387284Z",
+     "shell.execute_reply": "2021-03-09T19:44:11.386710Z"
+    },
+    "papermill": {
+     "duration": 0.1067,
+     "end_time": "2021-03-09T19:44:11.387417",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:11.280717",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.083738,
+     "end_time": "2021-03-09T19:44:11.555457",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:11.471719",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Dropping unnecesary columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:11.732569Z",
+     "iopub.status.busy": "2021-03-09T19:44:11.731868Z",
+     "iopub.status.idle": "2021-03-09T19:44:11.735400Z",
+     "shell.execute_reply": "2021-03-09T19:44:11.734855Z"
+    },
+    "papermill": {
+     "duration": 0.095893,
+     "end_time": "2021-03-09T19:44:11.735520",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:11.639627",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cols_to_drop = ['fnlwgt', 'education']\n",
+    "test_data.drop(cols_to_drop, inplace=True, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:11.925474Z",
+     "iopub.status.busy": "2021-03-09T19:44:11.924784Z",
+     "iopub.status.idle": "2021-03-09T19:44:11.929109Z",
+     "shell.execute_reply": "2021-03-09T19:44:11.928619Z"
+    },
+    "papermill": {
+     "duration": 0.107263,
+     "end_time": "2021-03-09T19:44:11.929231",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:11.821968",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>education.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>24900</th>\n",
+       "      <td>33</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6941</th>\n",
+       "      <td>47</td>\n",
+       "      <td>Local-gov</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Craft-repair</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26646</th>\n",
+       "      <td>25</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Craft-repair</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Amer-Indian-Eskimo</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5319</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21618</th>\n",
+       "      <td>26</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       age    workclass  education.num      marital.status         occupation  \\\n",
+       "24900   33      Private              9  Married-civ-spouse  Machine-op-inspct   \n",
+       "6941    47    Local-gov              9  Married-civ-spouse       Craft-repair   \n",
+       "26646   25      Private             11       Never-married       Craft-repair   \n",
+       "5319    52  Federal-gov              9            Divorced       Adm-clerical   \n",
+       "21618   26      Private             13       Never-married              Sales   \n",
+       "\n",
+       "        relationship                race     sex  capital.gain  capital.loss  \\\n",
+       "24900        Husband               White    Male             0             0   \n",
+       "6941         Husband               White    Male             0             0   \n",
+       "26646      Unmarried  Amer-Indian-Eskimo    Male             0             0   \n",
+       "5319   Not-in-family               White  Female             0             0   \n",
+       "21618  Not-in-family               White  Female             0             0   \n",
+       "\n",
+       "       hours.per.week native.country  income  \n",
+       "24900              40  United-States       1  \n",
+       "6941               40  United-States       0  \n",
+       "26646              40  United-States       0  \n",
+       "5319               43  United-States       0  \n",
+       "21618              48  United-States       0  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:12.106857Z",
+     "iopub.status.busy": "2021-03-09T19:44:12.105983Z",
+     "iopub.status.idle": "2021-03-09T19:44:12.109984Z",
+     "shell.execute_reply": "2021-03-09T19:44:12.109442Z"
+    },
+    "papermill": {
+     "duration": 0.095805,
+     "end_time": "2021-03-09T19:44:12.110102",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:12.014297",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_test = test_data.drop('income', axis=1)\n",
+    "y_test = test_data['income']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:12.282838Z",
+     "iopub.status.busy": "2021-03-09T19:44:12.282189Z",
+     "iopub.status.idle": "2021-03-09T19:44:12.391870Z",
+     "shell.execute_reply": "2021-03-09T19:44:12.392904Z"
+    },
+    "papermill": {
+     "duration": 0.19809,
+     "end_time": "2021-03-09T19:44:12.393107",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:12.195017",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.89      0.94      0.92      4945\n",
+      "           1       0.78      0.65      0.71      1568\n",
+      "\n",
+      "    accuracy                           0.87      6513\n",
+      "   macro avg       0.84      0.80      0.81      6513\n",
+      "weighted avg       0.87      0.87      0.87      6513\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = full_model.predict(X_test)\n",
+    "print(classification_report(y_test,predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2021-03-09T19:44:12.610496Z",
+     "iopub.status.busy": "2021-03-09T19:44:12.609829Z",
+     "iopub.status.idle": "2021-03-09T19:44:12.692094Z",
+     "shell.execute_reply": "2021-03-09T19:44:12.693098Z"
+    },
+    "papermill": {
+     "duration": 0.187941,
+     "end_time": "2021-03-09T19:44:12.693277",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:12.505336",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test AUC score is : 0.9269584691814037\n"
+     ]
+    }
+   ],
+   "source": [
+    "predicted_probabilities = full_model.predict_proba(X_test)\n",
+    "\n",
+    "print(f'Test AUC score is : {roc_auc_score(y_test,predicted_probabilities[:,1])}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.103253,
+     "end_time": "2021-03-09T19:44:12.910681",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:12.807428",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# 9. Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.087697,
+     "end_time": "2021-03-09T19:44:13.086079",
+     "exception": false,
+     "start_time": "2021-03-09T19:44:12.998382",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Our ROC_AUC is almost the same as in our train set: 0.928 versus 0.926.\n",
+    "\n",
+    "We can conclude that our model is not overfitted and we are very happy with the results."
+   ]
+  }
+ ],
+ "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"
+  },
+  "papermill": {
+   "duration": 260.709866,
+   "end_time": "2021-03-09T19:44:13.298522",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2021-03-09T19:39:52.588656",
+   "version": "2.1.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}