diff --git "a/AdultNoteBook/Kernels/AdaBoost/5-income-classification-using-meta-learning.ipynb" "b/AdultNoteBook/Kernels/AdaBoost/5-income-classification-using-meta-learning.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.061009,
+     "end_time": "2020-10-28T08:36:40.988052",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:40.927043",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Adult Census Income Classification using Meta Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:41.117414Z",
+     "iopub.status.busy": "2020-10-28T08:36:41.116495Z",
+     "iopub.status.idle": "2020-10-28T08:36:42.694440Z",
+     "shell.execute_reply": "2020-10-28T08:36:42.693723Z"
+    },
+    "papermill": {
+     "duration": 1.645861,
+     "end_time": "2020-10-28T08:36:42.694586",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:41.048725",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#importing the required libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from numpy import mean, std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from aif360.datasets import StandardDataset\n",
+    "from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric\n",
+    "import matplotlib.patches as patches\n",
+    "from aif360.algorithms.preprocessing import Reweighing\n",
+    "#from packages import *\n",
+    "#from ml_fairness import *\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "\n",
+    "from IPython.display import Markdown, display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:42.829035Z",
+     "iopub.status.busy": "2020-10-28T08:36:42.828189Z",
+     "iopub.status.idle": "2020-10-28T08:36:42.967836Z",
+     "shell.execute_reply": "2020-10-28T08:36:42.967005Z"
+    },
+    "papermill": {
+     "duration": 0.212609,
+     "end_time": "2020-10-28T08:36:42.967965",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:42.755356",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#reading the dataset and converting it to dataframe\n",
+    "df = pd.read_csv(\"../../Data/adult.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:43.109255Z",
+     "iopub.status.busy": "2020-10-28T08:36:43.108387Z",
+     "iopub.status.idle": "2020-10-28T08:36:43.123630Z",
+     "shell.execute_reply": "2020-10-28T08:36:43.122916Z"
+    },
+    "papermill": {
+     "duration": 0.096674,
+     "end_time": "2020-10-28T08:36:43.123760",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:43.027086",
+     "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>?</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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Viewing the top 5 rows of our dataset\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.060018,
+     "end_time": "2020-10-28T08:36:43.244776",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:43.184758",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.06009,
+     "end_time": "2020-10-28T08:36:43.365338",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:43.305248",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Income - Target column**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:43.497538Z",
+     "iopub.status.busy": "2020-10-28T08:36:43.496685Z",
+     "iopub.status.idle": "2020-10-28T08:36:43.680879Z",
+     "shell.execute_reply": "2020-10-28T08:36:43.680030Z"
+    },
+    "papermill": {
+     "duration": 0.255396,
+     "end_time": "2020-10-28T08:36:43.681032",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:43.425636",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='income', ylabel='count'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(df.income)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.071527,
+     "end_time": "2020-10-28T08:36:43.818119",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:43.746592",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "*As we can see, there is a **class imbalance**. The \">50K\" class is comparatively very less. So, we will do **Random Over-Sampling** during preprocessing.*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.068469,
+     "end_time": "2020-10-28T08:36:43.960681",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:43.892212",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Age**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:44.099436Z",
+     "iopub.status.busy": "2020-10-28T08:36:44.098260Z",
+     "iopub.status.idle": "2020-10-28T08:36:44.593106Z",
+     "shell.execute_reply": "2020-10-28T08:36:44.592040Z"
+    },
+    "papermill": {
+     "duration": 0.570153,
+     "end_time": "2020-10-28T08:36:44.593288",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:44.023135",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='age', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[df.income=='<=50K'].age, color='g')\n",
+    "sns.distplot(df[df.income=='>50K'].age, color='r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.063479,
+     "end_time": "2020-10-28T08:36:44.723916",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:44.660437",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "*We can observe a rough margin **around 30**. We will divide age into 2 parts ie. under 30 and over 30. We need to check if its useful for our model during testing.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.072609,
+     "end_time": "2020-10-28T08:36:44.869673",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:44.797064",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Workclass**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:45.015222Z",
+     "iopub.status.busy": "2020-10-28T08:36:45.014109Z",
+     "iopub.status.idle": "2020-10-28T08:36:45.300347Z",
+     "shell.execute_reply": "2020-10-28T08:36:45.299693Z"
+    },
+    "papermill": {
+     "duration": 0.366115,
+     "end_time": "2020-10-28T08:36:45.300483",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:44.934368",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='workclass', ylabel='count'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df.workclass, hue=df.income, palette='tab10')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.065421,
+     "end_time": "2020-10-28T08:36:45.431876",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:45.366455",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "*Majority of the data falls under **Private**. So, we will convert this into Private and not-Private.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.065404,
+     "end_time": "2020-10-28T08:36:45.563096",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:45.497692",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**fnlwgt**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:45.710016Z",
+     "iopub.status.busy": "2020-10-28T08:36:45.709172Z",
+     "iopub.status.idle": "2020-10-28T08:36:46.126864Z",
+     "shell.execute_reply": "2020-10-28T08:36:46.126062Z"
+    },
+    "papermill": {
+     "duration": 0.497341,
+     "end_time": "2020-10-28T08:36:46.127005",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:45.629664",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='fnlwgt', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[df.income=='<=50K'].fnlwgt, color='r')\n",
+    "sns.distplot(df[df.income=='>50K'].fnlwgt, color='g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.067156,
+     "end_time": "2020-10-28T08:36:46.261474",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:46.194318",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "*This is a very **ambiguous** attribute. Will check during testing.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.066207,
+     "end_time": "2020-10-28T08:36:46.394382",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:46.328175",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Education**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:46.542189Z",
+     "iopub.status.busy": "2020-10-28T08:36:46.540181Z",
+     "iopub.status.idle": "2020-10-28T08:36:46.894169Z",
+     "shell.execute_reply": "2020-10-28T08:36:46.894765Z"
+    },
+    "papermill": {
+     "duration": 0.433762,
+     "end_time": "2020-10-28T08:36:46.894983",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:46.461221",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='education', ylabel='count'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df.education, hue=df.income, palette='muted')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.069051,
+     "end_time": "2020-10-28T08:36:47.034493",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:46.965442",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**education.num**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:47.194005Z",
+     "iopub.status.busy": "2020-10-28T08:36:47.192908Z",
+     "iopub.status.idle": "2020-10-28T08:36:47.526601Z",
+     "shell.execute_reply": "2020-10-28T08:36:47.527461Z"
+    },
+    "papermill": {
+     "duration": 0.423139,
+     "end_time": "2020-10-28T08:36:47.527692",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:47.104553",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='education.num', ylabel='count'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(df[\"education.num\"], hue=df.income)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.070591,
+     "end_time": "2020-10-28T08:36:47.673540",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:47.602949",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**marital.status**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:47.832888Z",
+     "iopub.status.busy": "2020-10-28T08:36:47.831554Z",
+     "iopub.status.idle": "2020-10-28T08:36:48.091960Z",
+     "shell.execute_reply": "2020-10-28T08:36:48.091105Z"
+    },
+    "papermill": {
+     "duration": 0.347106,
+     "end_time": "2020-10-28T08:36:48.092150",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:47.745044",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='marital.status', ylabel='count'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df['marital.status'], hue=df.income)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.071711,
+     "end_time": "2020-10-28T08:36:48.236733",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:48.165022",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "*We observe that the majority of \">50K\" class is **Married-civ-spouse**. So we ll make it 1 and others 0*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.071897,
+     "end_time": "2020-10-28T08:36:48.381491",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:48.309594",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**occupation**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:48.539138Z",
+     "iopub.status.busy": "2020-10-28T08:36:48.538292Z",
+     "iopub.status.idle": "2020-10-28T08:36:48.903172Z",
+     "shell.execute_reply": "2020-10-28T08:36:48.902143Z"
+    },
+    "papermill": {
+     "duration": 0.44931,
+     "end_time": "2020-10-28T08:36:48.903381",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:48.454071",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='occupation', ylabel='count'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df.occupation, hue=df.income, palette='rocket')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.07383,
+     "end_time": "2020-10-28T08:36:49.052042",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:48.978212",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**relationship**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:49.216973Z",
+     "iopub.status.busy": "2020-10-28T08:36:49.216113Z",
+     "iopub.status.idle": "2020-10-28T08:36:49.471759Z",
+     "shell.execute_reply": "2020-10-28T08:36:49.471061Z"
+    },
+    "papermill": {
+     "duration": 0.342724,
+     "end_time": "2020-10-28T08:36:49.471980",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:49.129256",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='relationship', ylabel='count'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df.relationship, hue=df.income, palette='muted')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.075751,
+     "end_time": "2020-10-28T08:36:49.625632",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:49.549881",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**race**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:49.792885Z",
+     "iopub.status.busy": "2020-10-28T08:36:49.792036Z",
+     "iopub.status.idle": "2020-10-28T08:36:50.033477Z",
+     "shell.execute_reply": "2020-10-28T08:36:50.032747Z"
+    },
+    "papermill": {
+     "duration": 0.330461,
+     "end_time": "2020-10-28T08:36:50.033620",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:49.703159",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='race', ylabel='count'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df.race, hue=df.income, palette='Set2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.08034,
+     "end_time": "2020-10-28T08:36:50.191698",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:50.111358",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**sex**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:50.362273Z",
+     "iopub.status.busy": "2020-10-28T08:36:50.356259Z",
+     "iopub.status.idle": "2020-10-28T08:36:50.561310Z",
+     "shell.execute_reply": "2020-10-28T08:36:50.561922Z"
+    },
+    "papermill": {
+     "duration": 0.292381,
+     "end_time": "2020-10-28T08:36:50.562105",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:50.269724",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='sex', ylabel='count'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xticks(rotation=90)\n",
+    "sns.countplot(df.sex, hue=df.income)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.078795,
+     "end_time": "2020-10-28T08:36:50.720024",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:50.641229",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**capital.gain**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:50.888521Z",
+     "iopub.status.busy": "2020-10-28T08:36:50.887632Z",
+     "iopub.status.idle": "2020-10-28T08:36:50.896831Z",
+     "shell.execute_reply": "2020-10-28T08:36:50.896048Z"
+    },
+    "papermill": {
+     "duration": 0.097205,
+     "end_time": "2020-10-28T08:36:50.896980",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:50.799775",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        29849\n",
+       "15024      347\n",
+       "7688       284\n",
+       "7298       246\n",
+       "99999      159\n",
+       "         ...  \n",
+       "4931         1\n",
+       "1455         1\n",
+       "6097         1\n",
+       "22040        1\n",
+       "1111         1\n",
+       "Name: capital.gain, Length: 119, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['capital.gain'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.078989,
+     "end_time": "2020-10-28T08:36:51.055683",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:50.976694",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**capital.loss**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:51.221681Z",
+     "iopub.status.busy": "2020-10-28T08:36:51.220526Z",
+     "iopub.status.idle": "2020-10-28T08:36:51.230192Z",
+     "shell.execute_reply": "2020-10-28T08:36:51.229428Z"
+    },
+    "papermill": {
+     "duration": 0.095137,
+     "end_time": "2020-10-28T08:36:51.230334",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:51.135197",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       31042\n",
+       "1902      202\n",
+       "1977      168\n",
+       "1887      159\n",
+       "1848       51\n",
+       "        ...  \n",
+       "1411        1\n",
+       "1539        1\n",
+       "2472        1\n",
+       "1944        1\n",
+       "2201        1\n",
+       "Name: capital.loss, Length: 92, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['capital.loss'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.082267,
+     "end_time": "2020-10-28T08:36:51.462360",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:51.380093",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**hours.per.week**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:51.641955Z",
+     "iopub.status.busy": "2020-10-28T08:36:51.640904Z",
+     "iopub.status.idle": "2020-10-28T08:36:52.144354Z",
+     "shell.execute_reply": "2020-10-28T08:36:52.143562Z"
+    },
+    "papermill": {
+     "duration": 0.601217,
+     "end_time": "2020-10-28T08:36:52.144491",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:51.543274",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='hours.per.week', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[df.income=='<=50K']['hours.per.week'], color='b')\n",
+    "sns.distplot(df[df.income=='>50K']['hours.per.week'], color='r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.080915,
+     "end_time": "2020-10-28T08:36:52.306536",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:52.225621",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**native.country**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:52.487984Z",
+     "iopub.status.busy": "2020-10-28T08:36:52.486943Z",
+     "iopub.status.idle": "2020-10-28T08:36:52.491757Z",
+     "shell.execute_reply": "2020-10-28T08:36:52.492383Z"
+    },
+    "papermill": {
+     "duration": 0.105072,
+     "end_time": "2020-10-28T08:36:52.492545",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:52.387473",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "United-States                 29170\n",
+       "Mexico                          643\n",
+       "?                               583\n",
+       "Philippines                     198\n",
+       "Germany                         137\n",
+       "Canada                          121\n",
+       "Puerto-Rico                     114\n",
+       "El-Salvador                     106\n",
+       "India                           100\n",
+       "Cuba                             95\n",
+       "England                          90\n",
+       "Jamaica                          81\n",
+       "South                            80\n",
+       "China                            75\n",
+       "Italy                            73\n",
+       "Dominican-Republic               70\n",
+       "Vietnam                          67\n",
+       "Guatemala                        64\n",
+       "Japan                            62\n",
+       "Poland                           60\n",
+       "Columbia                         59\n",
+       "Taiwan                           51\n",
+       "Haiti                            44\n",
+       "Iran                             43\n",
+       "Portugal                         37\n",
+       "Nicaragua                        34\n",
+       "Peru                             31\n",
+       "Greece                           29\n",
+       "France                           29\n",
+       "Ecuador                          28\n",
+       "Ireland                          24\n",
+       "Hong                             20\n",
+       "Cambodia                         19\n",
+       "Trinadad&Tobago                  19\n",
+       "Laos                             18\n",
+       "Thailand                         18\n",
+       "Yugoslavia                       16\n",
+       "Outlying-US(Guam-USVI-etc)       14\n",
+       "Hungary                          13\n",
+       "Honduras                         13\n",
+       "Scotland                         12\n",
+       "Holand-Netherlands                1\n",
+       "Name: native.country, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['native.country'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.085343,
+     "end_time": "2020-10-28T08:36:52.659914",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:52.574571",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.082816,
+     "end_time": "2020-10-28T08:36:52.825695",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:52.742879",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Finding and Handling Missing Data\n",
+    "\n",
+    "*Observing the dataset, I found that the null values are marked as \"?\", So, we will now convert them to numpy.nan(null values).*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:53.001840Z",
+     "iopub.status.busy": "2020-10-28T08:36:53.001042Z",
+     "iopub.status.idle": "2020-10-28T08:36:53.264849Z",
+     "shell.execute_reply": "2020-10-28T08:36:53.265746Z"
+    },
+    "papermill": {
+     "duration": 0.356481,
+     "end_time": "2020-10-28T08:36:53.265954",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:52.909473",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values detected.\n",
+      "\n",
+      "workclass         1836\n",
+      "occupation        1843\n",
+      "native.country     583\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[df.select_dtypes(\"object\") ==\"?\"] = np.nan\n",
+    "nans = df.isnull().sum()\n",
+    "if len(nans[nans>0]):\n",
+    "    print(\"Missing values detected.\\n\")\n",
+    "    print(nans[nans>0])\n",
+    "else:\n",
+    "    print(\"No missing values. You are good to go.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:53.455267Z",
+     "iopub.status.busy": "2020-10-28T08:36:53.454105Z",
+     "iopub.status.idle": "2020-10-28T08:36:53.458991Z",
+     "shell.execute_reply": "2020-10-28T08:36:53.458022Z"
+    },
+    "papermill": {
+     "duration": 0.109928,
+     "end_time": "2020-10-28T08:36:53.459143",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:53.349215",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Handled missing values successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#majority of the values are \"Private\". Lets fill the missing values as \"Private\".\n",
+    "df.workclass.fillna(\"Private\", inplace=True)\n",
+    "\n",
+    "df.occupation.fillna(method='bfill', inplace=True)\n",
+    "\n",
+    "#majority of the values are \"United-States\". Lets fill the missing values as \"United-States\".\n",
+    "df['native.country'].fillna(\"United-States\", inplace=True)\n",
+    "\n",
+    "print(\"Handled missing values successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:53.640052Z",
+     "iopub.status.busy": "2020-10-28T08:36:53.639224Z",
+     "iopub.status.idle": "2020-10-28T08:36:53.643396Z",
+     "shell.execute_reply": "2020-10-28T08:36:53.642749Z"
+    },
+    "papermill": {
+     "duration": 0.099099,
+     "end_time": "2020-10-28T08:36:53.643539",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:53.544440",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.utils import column_or_1d\n",
+    "\n",
+    "class MyLabelEncoder(LabelEncoder):\n",
+    "\n",
+    "    def fit(self, y, arr=[]):\n",
+    "        y = column_or_1d(y, warn=True)\n",
+    "        if arr == []:\n",
+    "            arr=y\n",
+    "        self.classes_ = pd.Series(arr).unique()\n",
+    "        return self\n",
+    "\n",
+    "le = MyLabelEncoder()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.085395,
+     "end_time": "2020-10-28T08:36:53.812951",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:53.727556",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Feature Engineering and Encoding the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:54.013205Z",
+     "iopub.status.busy": "2020-10-28T08:36:54.008502Z",
+     "iopub.status.idle": "2020-10-28T08:36:54.359106Z",
+     "shell.execute_reply": "2020-10-28T08:36:54.358185Z"
+    },
+    "papermill": {
+     "duration": 0.459522,
+     "end_time": "2020-10-28T08:36:54.359290",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:53.899768",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Encoding complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# age_enc = pd.cut(df.age, bins=(0,25,45,65,100), labels=(0,1,2,3))\n",
+    "df['age_enc'] = df.age.apply(lambda x: 1 if x > 30 else 0)\n",
+    "\n",
+    "def prep_workclass(x):\n",
+    "    if x == 'Never-worked' or x == 'Without-pay':\n",
+    "        return 0\n",
+    "    elif x == 'Private':\n",
+    "        return 1\n",
+    "    elif x == 'State-gov' or x == 'Local-gov' or x == 'Federal-gov':\n",
+    "        return 2\n",
+    "    elif x == 'Self-emp-not-inc':\n",
+    "        return 3\n",
+    "    else:\n",
+    "        return 4\n",
+    "\n",
+    "df['workclass_enc'] = df.workclass.apply(prep_workclass)\n",
+    "\n",
+    "df['fnlwgt_enc'] = df.fnlwgt.apply(lambda x: 0 if x>200000 else 1)\n",
+    "\n",
+    "le.fit(df.education, arr=['Preschool', '1st-4th', '5th-6th', '7th-8th', '9th','10th', '11th', '12th', \n",
+    "                                             'HS-grad', 'Prof-school', 'Assoc-acdm', 'Assoc-voc', 'Some-college', 'Bachelors', 'Masters', 'Doctorate'])\n",
+    "df['education_enc'] = le.transform(df.education)\n",
+    "\n",
+    "\n",
+    "df['education.num_enc'] = df['education.num'].apply(lambda x: 1 if x>=9 else 0)\n",
+    "\n",
+    "df['marital.status_enc'] = df['marital.status'].apply(lambda x: 1 if x=='Married-civ-spouse' or x == 'Married-AF-spouse' else 0)\n",
+    "\n",
+    "def prep_occupation(x):\n",
+    "    if x in ['Prof-specialty', 'Exec-managerial', 'Tech-support', 'Protective-serv']:\n",
+    "        return 2\n",
+    "    elif x in ['Sales', 'Craft-repair']:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "df['occupation_enc'] = df.occupation.apply(prep_occupation)\n",
+    "\n",
+    "df['relationship_enc'] = df.relationship.apply(lambda x: 1 if x in ['Husband', 'Wife'] else 0)\n",
+    "\n",
+    "df['race_enc'] = df.race.apply(lambda x: 1 if x=='White' else 0)\n",
+    "\n",
+    "df['sex_enc'] = df.sex.apply(lambda x: 1 if x=='Male' else 0)\n",
+    "\n",
+    "df['capital.gain_enc'] = pd.cut(df[\"capital.gain\"], \n",
+    "                                bins=[-1,0,df[df[\"capital.gain\"]>0][\"capital.gain\"].median(), df[\"capital.gain\"].max()], labels=(0,1,2)).astype('int64')\n",
+    "\n",
+    "df['capital.loss_enc'] = pd.cut(df[\"capital.loss\"], \n",
+    "                                bins=[-1,0,df[df[\"capital.loss\"]>0][\"capital.loss\"].median(), df[\"capital.loss\"].max()], labels=(0,1,2)).astype('int64')\n",
+    "\n",
+    "# hpw_enc = pd.cut(df['hours.per.week'], bins= (0,30,40,53,168), labels=(0,1,2,3))\n",
+    "df['hours.per.week_enc'] = pd.qcut(df['hours.per.week'], q=5, labels=(0,1,2,3), duplicates='drop').astype('int64')\n",
+    "\n",
+    "df['native.country_enc'] = df['native.country'].apply(lambda x: 1 if x=='United-States' else 0)\n",
+    "\n",
+    "df['income_enc'] = df.income.apply(lambda x: 1 if x==\">50K\" else 0)\n",
+    "\n",
+    "print(\"Encoding complete.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:54.535166Z",
+     "iopub.status.busy": "2020-10-28T08:36:54.534328Z",
+     "iopub.status.idle": "2020-10-28T08:36:54.592946Z",
+     "shell.execute_reply": "2020-10-28T08:36:54.592090Z"
+    },
+    "papermill": {
+     "duration": 0.149425,
+     "end_time": "2020-10-28T08:36:54.593082",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:54.443657",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 32561 entries, 0 to 32560\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   workclass       32561 non-null  object\n",
+      " 1   education       32561 non-null  object\n",
+      " 2   marital.status  32561 non-null  object\n",
+      " 3   occupation      32561 non-null  object\n",
+      " 4   relationship    32561 non-null  object\n",
+      " 5   race            32561 non-null  object\n",
+      " 6   sex             32561 non-null  object\n",
+      " 7   native.country  32561 non-null  object\n",
+      " 8   income          32561 non-null  object\n",
+      "dtypes: object(9)\n",
+      "memory usage: 2.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.select_dtypes(\"object\").info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:54.824022Z",
+     "iopub.status.busy": "2020-10-28T08:36:54.823235Z",
+     "iopub.status.idle": "2020-10-28T08:36:54.827185Z",
+     "shell.execute_reply": "2020-10-28T08:36:54.826542Z"
+    },
+    "papermill": {
+     "duration": 0.134723,
+     "end_time": "2020-10-28T08:36:54.827324",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:54.692601",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#dropping encoded columns - education, sex, income\n",
+    "#df.drop(['education', 'sex', 'income'], 1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.084108,
+     "end_time": "2020-10-28T08:36:54.998236",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:54.914128",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Label Encoding without Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:55.194171Z",
+     "iopub.status.busy": "2020-10-28T08:36:55.182674Z",
+     "iopub.status.idle": "2020-10-28T08:36:55.265152Z",
+     "shell.execute_reply": "2020-10-28T08:36:55.264408Z"
+    },
+    "papermill": {
+     "duration": 0.181113,
+     "end_time": "2020-10-28T08:36:55.265288",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:55.084175",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "for feature in df.select_dtypes(\"object\").columns:\n",
+    "    df[feature]=le.fit_transform(df[feature])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.084307,
+     "end_time": "2020-10-28T08:36:55.435031",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:55.350724",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:55.612775Z",
+     "iopub.status.busy": "2020-10-28T08:36:55.611536Z",
+     "iopub.status.idle": "2020-10-28T08:36:55.636117Z",
+     "shell.execute_reply": "2020-10-28T08:36:55.635104Z"
+    },
+    "papermill": {
+     "duration": 0.116127,
+     "end_time": "2020-10-28T08:36:55.636286",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:55.520159",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 32561 entries, 0 to 32560\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column              Non-Null Count  Dtype\n",
+      "---  ------              --------------  -----\n",
+      " 0   age                 32561 non-null  int64\n",
+      " 1   workclass           32561 non-null  int32\n",
+      " 2   fnlwgt              32561 non-null  int64\n",
+      " 3   education           32561 non-null  int32\n",
+      " 4   education.num       32561 non-null  int64\n",
+      " 5   marital.status      32561 non-null  int32\n",
+      " 6   occupation          32561 non-null  int32\n",
+      " 7   relationship        32561 non-null  int32\n",
+      " 8   race                32561 non-null  int32\n",
+      " 9   sex                 32561 non-null  int32\n",
+      " 10  capital.gain        32561 non-null  int64\n",
+      " 11  capital.loss        32561 non-null  int64\n",
+      " 12  hours.per.week      32561 non-null  int64\n",
+      " 13  native.country      32561 non-null  int32\n",
+      " 14  income              32561 non-null  int32\n",
+      " 15  age_enc             32561 non-null  int64\n",
+      " 16  workclass_enc       32561 non-null  int64\n",
+      " 17  fnlwgt_enc          32561 non-null  int64\n",
+      " 18  education_enc       32561 non-null  int32\n",
+      " 19  education.num_enc   32561 non-null  int64\n",
+      " 20  marital.status_enc  32561 non-null  int64\n",
+      " 21  occupation_enc      32561 non-null  int64\n",
+      " 22  relationship_enc    32561 non-null  int64\n",
+      " 23  race_enc            32561 non-null  int64\n",
+      " 24  sex_enc             32561 non-null  int64\n",
+      " 25  capital.gain_enc    32561 non-null  int64\n",
+      " 26  capital.loss_enc    32561 non-null  int64\n",
+      " 27  hours.per.week_enc  32561 non-null  int64\n",
+      " 28  native.country_enc  32561 non-null  int64\n",
+      " 29  income_enc          32561 non-null  int64\n",
+      "dtypes: int32(10), int64(20)\n",
+      "memory usage: 6.2 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:55.823634Z",
+     "iopub.status.busy": "2020-10-28T08:36:55.822887Z",
+     "iopub.status.idle": "2020-10-28T08:36:56.167543Z",
+     "shell.execute_reply": "2020-10-28T08:36:56.166896Z"
+    },
+    "papermill": {
+     "duration": 0.44535,
+     "end_time": "2020-10-28T08:36:56.167669",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:55.722319",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 29 artists>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualizing the pearson correlation with the target class\n",
+    "pcorr = df.drop('income_enc',1).corrwith(df.income_enc)\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.title(\"Pearson Correlation of Features with Income\")\n",
+    "plt.xlabel(\"Features\")\n",
+    "plt.ylabel(\"Correlation Coeff\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.bar(pcorr.index, list(map(abs,pcorr.values)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.087728,
+     "end_time": "2020-10-28T08:36:56.343394",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:56.255666",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "From the pearson correlation plot, we can see that correlation of few columns are very **low** with the target column, so, we ll drop them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:56.528737Z",
+     "iopub.status.busy": "2020-10-28T08:36:56.527901Z",
+     "iopub.status.idle": "2020-10-28T08:36:56.531713Z",
+     "shell.execute_reply": "2020-10-28T08:36:56.530974Z"
+    },
+    "papermill": {
+     "duration": 0.101006,
+     "end_time": "2020-10-28T08:36:56.531862",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:56.430856",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.drop(['workclass', 'fnlwgt','occupation', 'race', 'native.country', 'fnlwgt_enc', 'race_enc', 'native.country_enc'], 1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:56.714687Z",
+     "iopub.status.busy": "2020-10-28T08:36:56.713949Z",
+     "iopub.status.idle": "2020-10-28T08:36:57.190831Z",
+     "shell.execute_reply": "2020-10-28T08:36:57.191425Z"
+    },
+    "papermill": {
+     "duration": 0.572038,
+     "end_time": "2020-10-28T08:36:57.191590",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:56.619552",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.corr().apply(abs))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.089285,
+     "end_time": "2020-10-28T08:36:57.370575",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:57.281290",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Dropping redundant features**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.089462,
+     "end_time": "2020-10-28T08:36:57.549962",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:57.460500",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "We can see that **education_enc, education.num_enc and education.num** as well as **relationship_enc and marital.status_enc** have **high correlation**. So, we will only keep one of them based on their correlation with income_enc.\n",
+    "\n",
+    "We also have some redundant feautres as we have engineered features from them(age, capital.gain, etc.)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:57.741439Z",
+     "iopub.status.busy": "2020-10-28T08:36:57.740253Z",
+     "iopub.status.idle": "2020-10-28T08:36:57.744370Z",
+     "shell.execute_reply": "2020-10-28T08:36:57.743586Z"
+    },
+    "papermill": {
+     "duration": 0.103287,
+     "end_time": "2020-10-28T08:36:57.744494",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:57.641207",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.drop(['age', 'education.num_enc', 'education_enc', 'marital.status_enc', 'capital.gain', 'capital.loss', 'hours.per.week'], 1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:57.933455Z",
+     "iopub.status.busy": "2020-10-28T08:36:57.932380Z",
+     "iopub.status.idle": "2020-10-28T08:36:57.946520Z",
+     "shell.execute_reply": "2020-10-28T08:36:57.945592Z"
+    },
+    "papermill": {
+     "duration": 0.111788,
+     "end_time": "2020-10-28T08:36:57.946715",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:57.834927",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 32561 entries, 0 to 32560\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column              Non-Null Count  Dtype\n",
+      "---  ------              --------------  -----\n",
+      " 0   education           32561 non-null  int32\n",
+      " 1   education.num       32561 non-null  int64\n",
+      " 2   marital.status      32561 non-null  int32\n",
+      " 3   relationship        32561 non-null  int32\n",
+      " 4   sex                 32561 non-null  int32\n",
+      " 5   income              32561 non-null  int32\n",
+      " 6   age_enc             32561 non-null  int64\n",
+      " 7   workclass_enc       32561 non-null  int64\n",
+      " 8   occupation_enc      32561 non-null  int64\n",
+      " 9   relationship_enc    32561 non-null  int64\n",
+      " 10  sex_enc             32561 non-null  int64\n",
+      " 11  capital.gain_enc    32561 non-null  int64\n",
+      " 12  capital.loss_enc    32561 non-null  int64\n",
+      " 13  hours.per.week_enc  32561 non-null  int64\n",
+      " 14  income_enc          32561 non-null  int64\n",
+      "dtypes: int32(5), int64(10)\n",
+      "memory usage: 3.1 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:58.138907Z",
+     "iopub.status.busy": "2020-10-28T08:36:58.138014Z",
+     "iopub.status.idle": "2020-10-28T08:36:58.141708Z",
+     "shell.execute_reply": "2020-10-28T08:36:58.140909Z"
+    },
+    "papermill": {
+     "duration": 0.103016,
+     "end_time": "2020-10-28T08:36:58.141871",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:58.038855",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X = df.drop('income_enc', 1)\n",
+    "y = df.income_enc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.091156,
+     "end_time": "2020-10-28T08:36:58.324773",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:58.233617",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Train Test Split (3:1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:58.515670Z",
+     "iopub.status.busy": "2020-10-28T08:36:58.514907Z",
+     "iopub.status.idle": "2020-10-28T08:36:58.549478Z",
+     "shell.execute_reply": "2020-10-28T08:36:58.548606Z"
+    },
+    "papermill": {
+     "duration": 0.133019,
+     "end_time": "2020-10-28T08:36:58.549630",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:58.416611",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:58.740649Z",
+     "iopub.status.busy": "2020-10-28T08:36:58.739701Z",
+     "iopub.status.idle": "2020-10-28T08:36:58.745922Z",
+     "shell.execute_reply": "2020-10-28T08:36:58.745252Z"
+    },
+    "papermill": {
+     "duration": 0.1047,
+     "end_time": "2020-10-28T08:36:58.746082",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:58.641382",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No. of rows in training data: 24420\n",
+      "No. of rows in testing data: 8141\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No. of rows in training data:\",X_train.shape[0])\n",
+    "print(\"No. of rows in testing data:\",X_test.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.093667,
+     "end_time": "2020-10-28T08:36:58.933943",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:58.840276",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Random Over Sampling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.09218,
+     "end_time": "2020-10-28T08:36:59.118540",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:59.026360",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "*We can see the class imbalance in our target. This results in models that have poor predictive performance, specifically for the minority class. So, we need to random over sampling*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:59.309918Z",
+     "iopub.status.busy": "2020-10-28T08:36:59.309133Z",
+     "iopub.status.idle": "2020-10-28T08:36:59.346776Z",
+     "shell.execute_reply": "2020-10-28T08:36:59.347364Z"
+    },
+    "papermill": {
+     "duration": 0.136876,
+     "end_time": "2020-10-28T08:36:59.347558",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:59.210682",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "oversample = RandomOverSampler(sampling_strategy=0.5) #50% oversampling\n",
+    "X_over, y_over = oversample.fit_resample(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:59.537147Z",
+     "iopub.status.busy": "2020-10-28T08:36:59.536332Z",
+     "iopub.status.idle": "2020-10-28T08:36:59.544316Z",
+     "shell.execute_reply": "2020-10-28T08:36:59.543692Z"
+    },
+    "papermill": {
+     "duration": 0.104993,
+     "end_time": "2020-10-28T08:36:59.544458",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:59.439465",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    18539\n",
+       "1     9269\n",
+       "Name: income_enc, dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_over.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.093829,
+     "end_time": "2020-10-28T08:36:59.731470",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:59.637641",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Model Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:36:59.930648Z",
+     "iopub.status.busy": "2020-10-28T08:36:59.929864Z",
+     "iopub.status.idle": "2020-10-28T08:37:00.853422Z",
+     "shell.execute_reply": "2020-10-28T08:37:00.852569Z"
+    },
+    "papermill": {
+     "duration": 1.028353,
+     "end_time": "2020-10-28T08:37:00.853577",
+     "exception": false,
+     "start_time": "2020-10-28T08:36:59.825224",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#Model Imports\n",
+    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import AdaBoostClassifier, ExtraTreesClassifier, GradientBoostingClassifier, RandomForestClassifier, StackingClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from lightgbm import LGBMClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:37:01.049850Z",
+     "iopub.status.busy": "2020-10-28T08:37:01.048910Z",
+     "iopub.status.idle": "2020-10-28T08:37:01.052367Z",
+     "shell.execute_reply": "2020-10-28T08:37:01.051558Z"
+    },
+    "papermill": {
+     "duration": 0.10278,
+     "end_time": "2020-10-28T08:37:01.052510",
+     "exception": false,
+     "start_time": "2020-10-28T08:37:00.949730",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "seed= 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:37:01.248252Z",
+     "iopub.status.busy": "2020-10-28T08:37:01.247432Z",
+     "iopub.status.idle": "2020-10-28T08:37:01.250643Z",
+     "shell.execute_reply": "2020-10-28T08:37:01.249891Z"
+    },
+    "papermill": {
+     "duration": 0.105051,
+     "end_time": "2020-10-28T08:37:01.250780",
+     "exception": false,
+     "start_time": "2020-10-28T08:37:01.145729",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "models = {\n",
+    "    'LR':LogisticRegression(random_state=seed),\n",
+    "    'SVC':SVC(random_state=seed),\n",
+    "    'AB':AdaBoostClassifier(random_state=seed),\n",
+    "    'ET':ExtraTreesClassifier(random_state=seed),\n",
+    "    'GB':GradientBoostingClassifier(random_state=seed),\n",
+    "    'RF':RandomForestClassifier(random_state=seed),\n",
+    "    'XGB':XGBClassifier(random_state=seed),\n",
+    "    'LGBM':LGBMClassifier(random_state=seed)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:37:01.449967Z",
+     "iopub.status.busy": "2020-10-28T08:37:01.447155Z",
+     "iopub.status.idle": "2020-10-28T08:37:01.454012Z",
+     "shell.execute_reply": "2020-10-28T08:37:01.453255Z"
+    },
+    "papermill": {
+     "duration": 0.109057,
+     "end_time": "2020-10-28T08:37:01.454142",
+     "exception": false,
+     "start_time": "2020-10-28T08:37:01.345085",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# evaluate a give model using cross-validation\n",
+    "def evaluate_models(model, xtrain, ytrain):\n",
+    "    cv = StratifiedKFold(shuffle=True, random_state=seed)\n",
+    "    scores = cross_val_score(model, xtrain, ytrain, scoring='accuracy', cv=cv, error_score='raise')\n",
+    "    return scores\n",
+    "\n",
+    "def plot_scores(xval,yval,show_value=False):\n",
+    "    plt.ylim(ymax = max(yval)+0.5, ymin = min(yval)-0.5)\n",
+    "    plt.xticks(rotation=45)\n",
+    "    s = sns.barplot(xval,yval)\n",
+    "    if show_value:\n",
+    "        for x,y in zip(range(len(yval)),yval):\n",
+    "            s.text(x,y+0.1,round(y,2),ha=\"center\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:37:01.655500Z",
+     "iopub.status.busy": "2020-10-28T08:37:01.654544Z",
+     "iopub.status.idle": "2020-10-28T08:38:25.313821Z",
+     "shell.execute_reply": "2020-10-28T08:38:25.314583Z"
+    },
+    "papermill": {
+     "duration": 83.767774,
+     "end_time": "2020-10-28T08:38:25.314830",
+     "exception": false,
+     "start_time": "2020-10-28T08:37:01.547056",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*LR 0.846 (0.003)\n",
+      "*SVC 0.846 (0.002)\n",
+      "*AB 0.852 (0.003)\n",
+      "*ET 0.842 (0.003)\n",
+      "*GB 0.854 (0.002)\n",
+      "*RF 0.842 (0.002)\n",
+      "*XGB 0.849 (0.002)\n",
+      "*LGBM 0.852 (0.002)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate the models and store results for 100% oversampled minority class\n",
+    "results, names = list(), list()\n",
+    "for name, model in models.items():\n",
+    "    scores = evaluate_models(model, X_train, y_train) \n",
+    "    results.append(scores) \n",
+    "    names.append(name) \n",
+    "    print('*%s %.3f (%.3f)' % (name, mean(scores), std(scores)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:25.557914Z",
+     "iopub.status.busy": "2020-10-28T08:38:25.553172Z",
+     "iopub.status.idle": "2020-10-28T08:38:25.804777Z",
+     "shell.execute_reply": "2020-10-28T08:38:25.804148Z"
+    },
+    "papermill": {
+     "duration": 0.372895,
+     "end_time": "2020-10-28T08:38:25.804938",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:25.432043",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(results, labels=names, showmeans=True)\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:26.020876Z",
+     "iopub.status.busy": "2020-10-28T08:38:26.018068Z",
+     "iopub.status.idle": "2020-10-28T08:38:26.024693Z",
+     "shell.execute_reply": "2020-10-28T08:38:26.023921Z"
+    },
+    "papermill": {
+     "duration": 0.120733,
+     "end_time": "2020-10-28T08:38:26.024859",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:25.904126",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "param_grids = {\n",
+    "    'LR':{'C':[0.001,0.01,0.1,1,10]},\n",
+    "    'SVC':{'gamma':[0.01,0.02,0.05,0.08,0.1], 'C':range(1,8)},\n",
+    "    \n",
+    "    'AB':{'learning_rate': [0.05, 0.1, 0.2], 'n_estimators': [100, 200, 500]},\n",
+    "    \n",
+    "    'ET':{'max_depth':[5,8,10,12], 'min_samples_split': [5,9,12],\n",
+    "          'n_estimators': [100,200,500,800]},\n",
+    "    \n",
+    "    'GB':{'learning_rate': [0.05, 0.1, 0.2], 'max_depth':[3,5,9],\n",
+    "          'min_samples_split': [5,7,9], 'n_estimators': [100,200,500],\n",
+    "          'subsample':[0.5,0.7,0.9]},\n",
+    "    \n",
+    "    'RF':{'max_depth':[3,5,9,15], 'n_estimators': [100, 200, 500, 1000],\n",
+    "          'learning_rate': [0.05, 0.1, 0.2], 'min_samples_split': [5,9,12]},\n",
+    "    \n",
+    "    'XGB':{'max_depth':[3,5,7,9], 'n_estimators': [100, 200, 500],\n",
+    "           'learning_rate': [0.05, 0.1, 0.2], 'subsample':[0.5,0.7,0.9]},\n",
+    "    \n",
+    "    'LGBM':{'n_estimators': [100,200,500],'learning_rate': [0.05, 0.1, 0.2],\n",
+    "            'subsample':[0.5,0.7,0.9],'num_leaves': [25,31,50]}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:26.230061Z",
+     "iopub.status.busy": "2020-10-28T08:38:26.228948Z",
+     "iopub.status.idle": "2020-10-28T08:38:26.232698Z",
+     "shell.execute_reply": "2020-10-28T08:38:26.231973Z"
+    },
+    "papermill": {
+     "duration": 0.108723,
+     "end_time": "2020-10-28T08:38:26.232885",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:26.124162",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# !pip install sklearn-deap\n",
+    "# from evolutionary_search import EvolutionaryAlgorithmSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:26.437390Z",
+     "iopub.status.busy": "2020-10-28T08:38:26.436290Z",
+     "iopub.status.idle": "2020-10-28T08:38:26.440095Z",
+     "shell.execute_reply": "2020-10-28T08:38:26.439459Z"
+    },
+    "papermill": {
+     "duration": 0.107748,
+     "end_time": "2020-10-28T08:38:26.440228",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:26.332480",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# evaluate the models and store results\n",
+    "# best_params = []\n",
+    "# names= []\n",
+    "# for name, param_grid, model in zip(param_grids.keys(), param_grids.values(), models.values()):\n",
+    "#     eascv = EvolutionaryAlgorithmSearchCV(model, param_grid, verbose=3, cv=3)\n",
+    "#     eascv.fit(X_train,y_train)\n",
+    "#     names.append(name)\n",
+    "#     best_params.append(eascv.best_params_)\n",
+    "#     print(name)\n",
+    "#     print(\"best score:\",eascv.best_score_)\n",
+    "#     print(\"best params:\",eascv.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:26.646981Z",
+     "iopub.status.busy": "2020-10-28T08:38:26.646165Z",
+     "iopub.status.idle": "2020-10-28T08:38:26.650939Z",
+     "shell.execute_reply": "2020-10-28T08:38:26.650309Z"
+    },
+    "papermill": {
+     "duration": 0.112455,
+     "end_time": "2020-10-28T08:38:26.651068",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:26.538613",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "best_params=[\n",
+    "    {'C': 10},\n",
+    "    {'gamma': 0.1, 'C': 2},\n",
+    "    {'learning_rate': 0.1, 'n_estimators': 500},\n",
+    "    {'max_depth': 12, 'min_samples_split': 9, 'n_estimators': 100},\n",
+    "    {'learning_rate': 0.05, 'max_depth': 3, 'min_samples_split': 9, 'n_estimators': 200, 'subsample': 0.9},\n",
+    "    {'max_depth': 9, 'n_estimators': 200, 'min_samples_split': 5},\n",
+    "    {'max_depth': 3, 'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.9},\n",
+    "    {'n_estimators': 100, 'learning_rate': 0.05, 'subsample': 0.9, 'num_leaves': 25}\n",
+    "            ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:26.859625Z",
+     "iopub.status.busy": "2020-10-28T08:38:26.858844Z",
+     "iopub.status.idle": "2020-10-28T08:38:26.862010Z",
+     "shell.execute_reply": "2020-10-28T08:38:26.862595Z"
+    },
+    "papermill": {
+     "duration": 0.111717,
+     "end_time": "2020-10-28T08:38:26.862792",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:26.751075",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "models = [\n",
+    "    ('LR',LogisticRegression(random_state=seed)),\n",
+    "    ('SVC',SVC(random_state=seed)),\n",
+    "    ('AB',AdaBoostClassifier(random_state=seed)),\n",
+    "    ('ET',ExtraTreesClassifier(random_state=seed)),\n",
+    "    ('GB',GradientBoostingClassifier(random_state=seed)),\n",
+    "    ('RF',RandomForestClassifier(random_state=seed)),\n",
+    "    ('XGB',XGBClassifier(random_state=seed)),\n",
+    "    ('LGBM',LGBMClassifier(random_state=seed))\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:27.074549Z",
+     "iopub.status.busy": "2020-10-28T08:38:27.073610Z",
+     "iopub.status.idle": "2020-10-28T08:38:27.077329Z",
+     "shell.execute_reply": "2020-10-28T08:38:27.076681Z"
+    },
+    "papermill": {
+     "duration": 0.112811,
+     "end_time": "2020-10-28T08:38:27.077476",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:26.964665",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "for model, param in zip(models, best_params):\n",
+    "    model[1].set_params(**param)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:27.284345Z",
+     "iopub.status.busy": "2020-10-28T08:38:27.283390Z",
+     "iopub.status.idle": "2020-10-28T08:38:27.286860Z",
+     "shell.execute_reply": "2020-10-28T08:38:27.286226Z"
+    },
+    "papermill": {
+     "duration": 0.110144,
+     "end_time": "2020-10-28T08:38:27.287007",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:27.176863",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "models.append(('MLModel',StackingClassifier(estimators = models[:-1])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:38:27.500726Z",
+     "iopub.status.busy": "2020-10-28T08:38:27.499937Z",
+     "iopub.status.idle": "2020-10-28T08:42:00.248175Z",
+     "shell.execute_reply": "2020-10-28T08:42:00.249017Z"
+    },
+    "papermill": {
+     "duration": 212.860946,
+     "end_time": "2020-10-28T08:42:00.249219",
+     "exception": false,
+     "start_time": "2020-10-28T08:38:27.388273",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LR trained.\n",
+      "SVC trained.\n",
+      "AB trained.\n",
+      "ET trained.\n",
+      "GB trained.\n",
+      "RF trained.\n",
+      "XGB trained.\n",
+      "LGBM trained.\n",
+      "MLModel trained.\n",
+      "Results are ready.\n"
+     ]
+    }
+   ],
+   "source": [
+    "scores=[]\n",
+    "preds=[]\n",
+    "for model in models:\n",
+    "    model[1].fit(X_train,y_train)\n",
+    "    print(model[0],\"trained.\")\n",
+    "    scores.append(model[1].score(X_test,y_test))\n",
+    "    preds.append(model[1].predict(X_test))\n",
+    "print(\"Results are ready.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.10984,
+     "end_time": "2020-10-28T08:42:00.506117",
+     "exception": false,
+     "start_time": "2020-10-28T08:42:00.396277",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Using Classification Based on Assocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:42:00.722046Z",
+     "iopub.status.busy": "2020-10-28T08:42:00.721039Z",
+     "iopub.status.idle": "2020-10-28T08:42:42.727019Z",
+     "shell.execute_reply": "2020-10-28T08:42:42.726220Z"
+    },
+    "papermill": {
+     "duration": 42.117479,
+     "end_time": "2020-10-28T08:42:42.727207",
+     "exception": false,
+     "start_time": "2020-10-28T08:42:00.609728",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting pyarc==1.0.23\r\n",
+      "  Downloading pyarc-1.0.23-py2.py3-none-any.whl (23 kB)\r\n",
+      "Requirement already satisfied: sklearn in /opt/conda/lib/python3.7/site-packages (from pyarc==1.0.23) (0.0)\r\n",
+      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from pyarc==1.0.23) (1.1.2)\r\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from pyarc==1.0.23) (1.18.5)\r\n",
+      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from sklearn->pyarc==1.0.23) (0.23.2)\r\n",
+      "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas->pyarc==1.0.23) (2019.3)\r\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->pyarc==1.0.23) (2.8.1)\r\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sklearn->pyarc==1.0.23) (1.4.1)\r\n",
+      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sklearn->pyarc==1.0.23) (0.14.1)\r\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->sklearn->pyarc==1.0.23) (2.1.0)\r\n",
+      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->pyarc==1.0.23) (1.14.0)\r\n",
+      "Installing collected packages: pyarc\r\n",
+      "Successfully installed pyarc-1.0.23\r\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\r\n",
+      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n",
+      "Collecting pyfim\r\n",
+      "  Downloading pyfim-6.28.tar.gz (357 kB)\r\n",
+      "\u001b[K     |████████████████████████████████| 357 kB 585 kB/s \r\n",
+      "\u001b[?25hBuilding wheels for collected packages: pyfim\r\n",
+      "  Building wheel for pyfim (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\r\n",
+      "\u001b[?25h  Created wheel for pyfim: filename=pyfim-6.28-cp37-cp37m-linux_x86_64.whl size=683916 sha256=7cffa03e3a8b7fc066630ddbed15ac56640f4b167c70f1824b2a05f440233257\r\n",
+      "  Stored in directory: /root/.cache/pip/wheels/08/9f/26/09cb4efd027e46f96e0a0f33d0a74be614d3caf89c1eeb75a8\r\n",
+      "Successfully built pyfim\r\n",
+      "Installing collected packages: pyfim\r\n",
+      "Successfully installed pyfim-6.28\r\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\r\n",
+      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pyarc==1.0.23\n",
+    "!pip install pyfim\n",
+    "from pyarc import CBA, TransactionDB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:42:43.006759Z",
+     "iopub.status.busy": "2020-10-28T08:42:43.005945Z",
+     "iopub.status.idle": "2020-10-28T08:42:49.716544Z",
+     "shell.execute_reply": "2020-10-28T08:42:49.715855Z"
+    },
+    "papermill": {
+     "duration": 6.846881,
+     "end_time": "2020-10-28T08:42:49.716705",
+     "exception": false,
+     "start_time": "2020-10-28T08:42:42.869824",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pyarc.cba.CBA at 0x7f51143f3cd0>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "txns_train = TransactionDB.from_DataFrame(X_train.join(y_train))\n",
+    "txns_test = TransactionDB.from_DataFrame(X_test.join(y_test))\n",
+    "\n",
+    "\n",
+    "cba = CBA(support=0.15, confidence=0.5, algorithm=\"m1\")\n",
+    "cba.fit(txns_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:42:49.986351Z",
+     "iopub.status.busy": "2020-10-28T08:42:49.981225Z",
+     "iopub.status.idle": "2020-10-28T08:42:50.662022Z",
+     "shell.execute_reply": "2020-10-28T08:42:50.662616Z"
+    },
+    "papermill": {
+     "duration": 0.819005,
+     "end_time": "2020-10-28T08:42:50.662874",
+     "exception": false,
+     "start_time": "2020-10-28T08:42:49.843869",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cba_score = cba.rule_model_accuracy(txns_test) \n",
+    "scores.append(cba_score)\n",
+    "models.append([\"CBA\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:42:50.925779Z",
+     "iopub.status.busy": "2020-10-28T08:42:50.925042Z",
+     "iopub.status.idle": "2020-10-28T08:42:50.928771Z",
+     "shell.execute_reply": "2020-10-28T08:42:50.928171Z"
+    },
+    "papermill": {
+     "duration": 0.139087,
+     "end_time": "2020-10-28T08:42:50.928948",
+     "exception": false,
+     "start_time": "2020-10-28T08:42:50.789861",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model_names= [i[0] for i in models]\n",
+    "scores = list(map(lambda x: x*100, scores))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-28T08:42:51.196084Z",
+     "iopub.status.busy": "2020-10-28T08:42:51.193798Z",
+     "iopub.status.idle": "2020-10-28T08:42:51.403573Z",
+     "shell.execute_reply": "2020-10-28T08:42:51.402846Z"
+    },
+    "papermill": {
+     "duration": 0.348816,
+     "end_time": "2020-10-28T08:42:51.403717",
+     "exception": false,
+     "start_time": "2020-10-28T08:42:51.054901",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_scores(model_names, scores, True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fairness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This DataFrame is created to stock differents models and fair metrics that we produce in this notebook\n",
+    "algo_metrics = pd.DataFrame(columns=['model', 'fair_metrics', 'prediction', 'probs'])\n",
+    "\n",
+    "def add_to_df_algo_metrics(algo_metrics, model, fair_metrics, preds, probs, name):\n",
+    "    return algo_metrics.append(pd.DataFrame(data=[[model, fair_metrics, preds, probs]], columns=['model', 'fair_metrics', 'prediction', 'probs'], index=[name]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fair_metrics(dataset, pred, pred_is_dataset=False):\n",
+    "    if pred_is_dataset:\n",
+    "        dataset_pred = pred\n",
+    "    else:\n",
+    "        dataset_pred = dataset.copy()\n",
+    "        dataset_pred.labels = pred\n",
+    "    \n",
+    "    cols = ['statistical_parity_difference', 'equal_opportunity_difference', 'average_abs_odds_difference',  'disparate_impact', 'theil_index']\n",
+    "    obj_fairness = [[0,0,0,1,0]]\n",
+    "    \n",
+    "    fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols)\n",
+    "    \n",
+    "    for attr in dataset_pred.protected_attribute_names:\n",
+    "        idx = dataset_pred.protected_attribute_names.index(attr)\n",
+    "        privileged_groups =  [{attr:dataset_pred.privileged_protected_attributes[idx][0]}] \n",
+    "        unprivileged_groups = [{attr:dataset_pred.unprivileged_protected_attributes[idx][0]}] \n",
+    "        \n",
+    "        classified_metric = ClassificationMetric(dataset, \n",
+    "                                                     dataset_pred,\n",
+    "                                                     unprivileged_groups=unprivileged_groups,\n",
+    "                                                     privileged_groups=privileged_groups)\n",
+    "\n",
+    "        metric_pred = BinaryLabelDatasetMetric(dataset_pred,\n",
+    "                                                     unprivileged_groups=unprivileged_groups,\n",
+    "                                                     privileged_groups=privileged_groups)\n",
+    "\n",
+    "        acc = classified_metric.accuracy()\n",
+    "\n",
+    "        row = pd.DataFrame([[metric_pred.mean_difference(),\n",
+    "                                classified_metric.equal_opportunity_difference(),\n",
+    "                                classified_metric.average_abs_odds_difference(),\n",
+    "                                metric_pred.disparate_impact(),\n",
+    "                                classified_metric.theil_index()]],\n",
+    "                           columns  = cols,\n",
+    "                           index = [attr]\n",
+    "                          )\n",
+    "        fair_metrics = fair_metrics.append(row)    \n",
+    "    \n",
+    "    fair_metrics = fair_metrics.replace([-np.inf, np.inf], 2)\n",
+    "        \n",
+    "    return fair_metrics\n",
+    "\n",
+    "def plot_fair_metrics(fair_metrics):\n",
+    "    fig, ax = plt.subplots(figsize=(20,4), ncols=5, nrows=1)\n",
+    "\n",
+    "    plt.subplots_adjust(\n",
+    "        left    =  0.125, \n",
+    "        bottom  =  0.1, \n",
+    "        right   =  0.9, \n",
+    "        top     =  0.9, \n",
+    "        wspace  =  .5, \n",
+    "        hspace  =  1.1\n",
+    "    )\n",
+    "\n",
+    "    y_title_margin = 1.2\n",
+    "\n",
+    "    plt.suptitle(\"Fairness metrics\", y = 1.09, fontsize=20)\n",
+    "    sns.set(style=\"dark\")\n",
+    "\n",
+    "    cols = fair_metrics.columns.values\n",
+    "    obj = fair_metrics.loc['objective']\n",
+    "    size_rect = [0.2,0.2,0.2,0.4,0.25]\n",
+    "    rect = [-0.1,-0.1,-0.1,0.8,0]\n",
+    "    bottom = [-1,-1,-1,0,0]\n",
+    "    top = [1,1,1,2,1]\n",
+    "    bound = [[-0.1,0.1],[-0.1,0.1],[-0.1,0.1],[0.8,1.2],[0,0.25]]\n",
+    "\n",
+    "    display(Markdown(\"### Check bias metrics :\"))\n",
+    "    display(Markdown(\"A model can be considered bias if just one of these five metrics show that this model is biased.\"))\n",
+    "    for attr in fair_metrics.index[1:len(fair_metrics)].values:\n",
+    "        display(Markdown(\"#### For the %s attribute :\"%attr))\n",
+    "        check = [bound[i][0] < fair_metrics.loc[attr][i] < bound[i][1] for i in range(0,5)]\n",
+    "        display(Markdown(\"With default thresholds, bias against unprivileged group detected in **%d** out of 5 metrics\"%(5 - sum(check))))\n",
+    "\n",
+    "    for i in range(0,5):\n",
+    "        plt.subplot(1, 5, i+1)\n",
+    "        ax = sns.barplot(x=fair_metrics.index[1:len(fair_metrics)], y=fair_metrics.iloc[1:len(fair_metrics)][cols[i]])\n",
+    "        \n",
+    "        for j in range(0,len(fair_metrics)-1):\n",
+    "            a, val = ax.patches[j], fair_metrics.iloc[j+1][cols[i]]\n",
+    "            marg = -0.2 if val < 0 else 0.1\n",
+    "            ax.text(a.get_x()+a.get_width()/5, a.get_y()+a.get_height()+marg, round(val, 3), fontsize=15,color='black')\n",
+    "\n",
+    "        plt.ylim(bottom[i], top[i])\n",
+    "        plt.setp(ax.patches, linewidth=0)\n",
+    "        ax.add_patch(patches.Rectangle((-5,rect[i]), 10, size_rect[i], alpha=0.3, facecolor=\"green\", linewidth=1, linestyle='solid'))\n",
+    "        plt.axhline(obj[i], color='black', alpha=0.3)\n",
+    "        plt.title(cols[i])\n",
+    "        ax.set_ylabel('')    \n",
+    "        ax.set_xlabel('')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_fair_metrics_and_plot(data, model, plot=False, model_aif=False):\n",
+    "    pred = model.predict(data).labels if model_aif else model.predict(data.features)\n",
+    "    # fair_metrics function available in the metrics.py file\n",
+    "    fair = fair_metrics(data, pred)\n",
+    "\n",
+    "    if plot:\n",
+    "        # plot_fair_metrics function available in the visualisations.py file\n",
+    "        # The visualisation of this function is inspired by the dashboard on the demo of IBM aif360 \n",
+    "        plot_fair_metrics(fair)\n",
+    "        display(fair)\n",
+    "    \n",
+    "    return fair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##train['Sex'] = train['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
+    "df.head()\n",
+    "\n",
+    "df = df.drop(['income','sex'], axis = 1)\n",
+    "\n",
+    "#features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\", \"Survived\"]\n",
+    "#X = pd.get_dummies(train_data[features])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>education</th>\n",
+       "      <th>education.num</th>\n",
+       "      <th>marital.status</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>age_enc</th>\n",
+       "      <th>workclass_enc</th>\n",
+       "      <th>occupation_enc</th>\n",
+       "      <th>relationship_enc</th>\n",
+       "      <th>sex_enc</th>\n",
+       "      <th>capital.gain_enc</th>\n",
+       "      <th>capital.loss_enc</th>\n",
+       "      <th>hours.per.week_enc</th>\n",
+       "      <th>income_enc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32556</th>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32557</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32558</th>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32559</th>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32560</th>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>32561 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       education  education.num  marital.status  relationship  age_enc  \\\n",
+       "0             11              9               6             1        1   \n",
+       "1             11              9               6             1        1   \n",
+       "2             15             10               6             4        1   \n",
+       "3              5              4               0             4        1   \n",
+       "4             15             10               5             3        1   \n",
+       "...          ...            ...             ...           ...      ...   \n",
+       "32556         15             10               4             1        0   \n",
+       "32557          7             12               2             5        0   \n",
+       "32558         11              9               2             0        1   \n",
+       "32559         11              9               6             4        1   \n",
+       "32560         11              9               4             3        0   \n",
+       "\n",
+       "       workclass_enc  occupation_enc  relationship_enc  sex_enc  \\\n",
+       "0                  1               2                 0        0   \n",
+       "1                  1               2                 0        0   \n",
+       "2                  1               0                 0        0   \n",
+       "3                  1               0                 0        0   \n",
+       "4                  1               2                 0        0   \n",
+       "...              ...             ...               ...      ...   \n",
+       "32556              1               2                 0        1   \n",
+       "32557              1               2                 1        0   \n",
+       "32558              1               0                 1        1   \n",
+       "32559              1               0                 0        0   \n",
+       "32560              1               0                 0        1   \n",
+       "\n",
+       "       capital.gain_enc  capital.loss_enc  hours.per.week_enc  income_enc  \n",
+       "0                     0                 2                   1           0  \n",
+       "1                     0                 2                   0           0  \n",
+       "2                     0                 2                   1           0  \n",
+       "3                     0                 2                   1           0  \n",
+       "4                     0                 2                   1           0  \n",
+       "...                 ...               ...                 ...         ...  \n",
+       "32556                 0                 0                   1           0  \n",
+       "32557                 0                 0                   1           0  \n",
+       "32558                 0                 0                   1           1  \n",
+       "32559                 0                 0                   1           0  \n",
+       "32560                 0                 0                   0           0  \n",
+       "\n",
+       "[32561 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "privileged_groups = [{'sex_enc': 1}]\n",
+    "unprivileged_groups = [{'sex_enc': 0}]\n",
+    "dataset_orig = StandardDataset(df,\n",
+    "                                  label_name='income_enc',\n",
+    "                                  protected_attribute_names=['sex_enc'],\n",
+    "                                  favorable_classes=[1],\n",
+    "                                  privileged_classes=[[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "#### Original training dataset"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Difference in mean outcomes between unprivileged and privileged groups = -0.196276\n"
+     ]
+    }
+   ],
+   "source": [
+    "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig, \n",
+    "                                             unprivileged_groups=unprivileged_groups,\n",
+    "                                             privileged_groups=privileged_groups)\n",
+    "display(Markdown(\"#### Original training dataset\"))\n",
+    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipynbname\n",
+    "nb_fname = ipynbname.name()\n",
+    "nb_path = ipynbname.path()\n",
+    "\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "import pickle\n",
+    "\n",
+    "data_orig_train, data_orig_test = dataset_orig.split([0.7], shuffle=True)\n",
+    "X_train = data_orig_train.features\n",
+    "y_train = data_orig_train.labels.ravel()\n",
+    "\n",
+    "X_test = data_orig_test.features\n",
+    "y_test = data_orig_test.labels.ravel()\n",
+    "num_estimators = 500\n",
+    "\n",
+    "model = AdaBoostClassifier(learning_rate = 0.1, n_estimators = 1)\n",
+    "\n",
+    "mdl = model.fit(X_train, y_train)\n",
+    "with open('../../Results/AdaBoost/' + nb_fname + '.pkl', 'wb') as f:\n",
+    "        pickle.dump(mdl, f)\n",
+    "\n",
+    "with open('../../Results/AdaBoost/' + nb_fname + '_Train' + '.pkl', 'wb') as f:\n",
+    "    pickle.dump(data_orig_train, f) \n",
+    "    \n",
+    "with open('../../Results/AdaBoost/' + nb_fname + '_Test' + '.pkl', 'wb') as f:\n",
+    "    pickle.dump(data_orig_test, f) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "invalid value encountered in double_scalars\n",
+      "invalid value encountered in double_scalars\n",
+      "invalid value encountered in double_scalars\n",
+      "invalid value encountered in double_scalars\n",
+      "invalid value encountered in double_scalars\n",
+      "invalid value encountered in double_scalars\n"
+     ]
+    }
+   ],
+   "source": [
+    "from csv import writer\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "final_metrics = []\n",
+    "accuracy = []\n",
+    "f1= []\n",
+    "\n",
+    "for i in range(1,num_estimators+1):\n",
+    "    \n",
+    "    model = AdaBoostClassifier(learning_rate = 0.1, n_estimators= i)\n",
+    "    \n",
+    "    mdl = model.fit(X_train, y_train)\n",
+    "    yy = mdl.predict(X_test)\n",
+    "    accuracy.append(accuracy_score(y_test, yy))\n",
+    "    f1.append(f1_score(y_test, yy))\n",
+    "    fair = get_fair_metrics_and_plot(data_orig_test, mdl)                           \n",
+    "    fair_list = fair.iloc[1].tolist()\n",
+    "    #fair_list.insert(0, i)\n",
+    "    final_metrics.append(fair_list)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            0         1         2         3         4\n",
+      "0    0.000000  0.000000  0.000000       NaN  0.275709\n",
+      "1    0.000000  0.000000  0.000000       NaN  0.275709\n",
+      "2    0.000000  0.000000  0.000000       NaN  0.275709\n",
+      "3    0.000000  0.000000  0.000000       NaN  0.275709\n",
+      "4    0.000000  0.000000  0.000000       NaN  0.275709\n",
+      "..        ...       ...       ...       ...       ...\n",
+      "495 -0.181288 -0.154158  0.114867  0.266184  0.126698\n",
+      "496 -0.181288 -0.154158  0.114867  0.266184  0.126698\n",
+      "497 -0.181288 -0.154158  0.114867  0.266184  0.126698\n",
+      "498 -0.181288 -0.154158  0.114867  0.266184  0.126698\n",
+      "499 -0.181288 -0.154158  0.114867  0.266184  0.126698\n",
+      "\n",
+      "[500 rows x 5 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>classifier</th>\n",
+       "      <th>T0</th>\n",
+       "      <th>T1</th>\n",
+       "      <th>T2</th>\n",
+       "      <th>T3</th>\n",
+       "      <th>T4</th>\n",
+       "      <th>T5</th>\n",
+       "      <th>T6</th>\n",
+       "      <th>T7</th>\n",
+       "      <th>T8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>T490</th>\n",
+       "      <th>T491</th>\n",
+       "      <th>T492</th>\n",
+       "      <th>T493</th>\n",
+       "      <th>T494</th>\n",
+       "      <th>T495</th>\n",
+       "      <th>T496</th>\n",
+       "      <th>T497</th>\n",
+       "      <th>T498</th>\n",
+       "      <th>T499</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.759034</td>\n",
+       "      <td>0.759034</td>\n",
+       "      <td>0.759034</td>\n",
+       "      <td>0.759034</td>\n",
+       "      <td>0.759034</td>\n",
+       "      <td>0.759034</td>\n",
+       "      <td>0.816153</td>\n",
+       "      <td>0.816153</td>\n",
+       "      <td>0.785239</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "      <td>0.848091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1</th>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.508753</td>\n",
+       "      <td>0.508753</td>\n",
+       "      <td>0.197399</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "      <td>0.646161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistical_parity_difference</th>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.130704</td>\n",
+       "      <td>-0.130704</td>\n",
+       "      <td>-0.028605</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "      <td>-0.181288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equal_opportunity_difference</th>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.126130</td>\n",
+       "      <td>-0.126130</td>\n",
+       "      <td>-0.051797</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "      <td>-0.154158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>average_abs_odds_difference</th>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.091730</td>\n",
+       "      <td>0.091730</td>\n",
+       "      <td>0.026116</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "      <td>0.114867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>disparate_impact</th>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.363891</td>\n",
+       "      <td>-1.363891</td>\n",
+       "      <td>-1.596109</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "      <td>-1.323566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theil_index</th>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.275709</td>\n",
+       "      <td>0.275709</td>\n",
+       "      <td>0.275709</td>\n",
+       "      <td>0.275709</td>\n",
+       "      <td>0.275709</td>\n",
+       "      <td>0.275709</td>\n",
+       "      <td>0.173099</td>\n",
+       "      <td>0.173099</td>\n",
+       "      <td>0.241607</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "      <td>0.126698</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 501 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               classifier        T0        T1        T2  \\\n",
+       "accuracy                         0.848091  0.759034  0.759034  0.759034   \n",
+       "f1                               0.646161  0.000000  0.000000  0.000000   \n",
+       "statistical_parity_difference   -0.181288  0.000000  0.000000  0.000000   \n",
+       "equal_opportunity_difference    -0.154158  0.000000  0.000000  0.000000   \n",
+       "average_abs_odds_difference      0.114867  0.000000  0.000000  0.000000   \n",
+       "disparate_impact                -1.323566       NaN       NaN       NaN   \n",
+       "theil_index                      0.126698  0.275709  0.275709  0.275709   \n",
+       "\n",
+       "                                     T3        T4        T5        T6  \\\n",
+       "accuracy                       0.759034  0.759034  0.759034  0.816153   \n",
+       "f1                             0.000000  0.000000  0.000000  0.508753   \n",
+       "statistical_parity_difference  0.000000  0.000000  0.000000 -0.130704   \n",
+       "equal_opportunity_difference   0.000000  0.000000  0.000000 -0.126130   \n",
+       "average_abs_odds_difference    0.000000  0.000000  0.000000  0.091730   \n",
+       "disparate_impact                    NaN       NaN       NaN -1.363891   \n",
+       "theil_index                    0.275709  0.275709  0.275709  0.173099   \n",
+       "\n",
+       "                                     T7        T8  ...      T490      T491  \\\n",
+       "accuracy                       0.816153  0.785239  ...  0.848091  0.848091   \n",
+       "f1                             0.508753  0.197399  ...  0.646161  0.646161   \n",
+       "statistical_parity_difference -0.130704 -0.028605  ... -0.181288 -0.181288   \n",
+       "equal_opportunity_difference  -0.126130 -0.051797  ... -0.154158 -0.154158   \n",
+       "average_abs_odds_difference    0.091730  0.026116  ...  0.114867  0.114867   \n",
+       "disparate_impact              -1.363891 -1.596109  ... -1.323566 -1.323566   \n",
+       "theil_index                    0.173099  0.241607  ...  0.126698  0.126698   \n",
+       "\n",
+       "                                   T492      T493      T494      T495  \\\n",
+       "accuracy                       0.848091  0.848091  0.848091  0.848091   \n",
+       "f1                             0.646161  0.646161  0.646161  0.646161   \n",
+       "statistical_parity_difference -0.181288 -0.181288 -0.181288 -0.181288   \n",
+       "equal_opportunity_difference  -0.154158 -0.154158 -0.154158 -0.154158   \n",
+       "average_abs_odds_difference    0.114867  0.114867  0.114867  0.114867   \n",
+       "disparate_impact              -1.323566 -1.323566 -1.323566 -1.323566   \n",
+       "theil_index                    0.126698  0.126698  0.126698  0.126698   \n",
+       "\n",
+       "                                   T496      T497      T498      T499  \n",
+       "accuracy                       0.848091  0.848091  0.848091  0.848091  \n",
+       "f1                             0.646161  0.646161  0.646161  0.646161  \n",
+       "statistical_parity_difference -0.181288 -0.181288 -0.181288 -0.181288  \n",
+       "equal_opportunity_difference  -0.154158 -0.154158 -0.154158 -0.154158  \n",
+       "average_abs_odds_difference    0.114867  0.114867  0.114867  0.114867  \n",
+       "disparate_impact              -1.323566 -1.323566 -1.323566 -1.323566  \n",
+       "theil_index                    0.126698  0.126698  0.126698  0.126698  \n",
+       "\n",
+       "[7 rows x 501 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "final_result = pd.DataFrame(final_metrics)\n",
+    "print(final_result)\n",
+    "final_result[3] = np.log(final_result[3])\n",
+    "final_result = final_result.transpose()\n",
+    "acc_f1 = pd.DataFrame(accuracy)\n",
+    "acc_f1['f1'] = f1\n",
+    "acc_f1 = pd.DataFrame(acc_f1).transpose()\n",
+    "acc = acc_f1.rename(index={0: 'accuracy', 1: 'f1'})\n",
+    "final_result = final_result.rename(index={0: 'statistical_parity_difference', 1: 'equal_opportunity_difference', 2: 'average_abs_odds_difference', 3: 'disparate_impact', 4: 'theil_index'})\n",
+    "final_result = pd.concat([acc,final_result])\n",
+    "final_result.columns = ['T' + str(col) for col in final_result.columns]\n",
+    "final_result.insert(0, \"classifier\", final_result['T' + str(num_estimators - 1)])   ##Add final metrics add the beginning of the df\n",
+    "final_result.to_csv('../../Results/AdaBoost/' + nb_fname + '.csv')\n",
+    "final_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "papermill": {
+   "duration": 375.856383,
+   "end_time": "2020-10-28T08:42:51.640901",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2020-10-28T08:36:35.784518",
+   "version": "2.1.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}