diff --git "a/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/6-income-prediction-eda-to-visuals-0-98-auc-checkpoint.ipynb" "b/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/6-income-prediction-eda-to-visuals-0-98-auc-checkpoint.ipynb" new file mode 100644--- /dev/null +++ "b/AdultNoteBook/Kernels/AdaBoost/.ipynb_checkpoints/6-income-prediction-eda-to-visuals-0-98-auc-checkpoint.ipynb" @@ -0,0 +1,5135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "papermill": { + "duration": 0.065363, + "end_time": "2021-05-22T12:30:06.118296", + "exception": false, + "start_time": "2021-05-22T12:30:06.052933", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Importing Important libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:06.250356Z", + "iopub.status.busy": "2021-05-22T12:30:06.249702Z", + "iopub.status.idle": "2021-05-22T12:30:08.388760Z", + "shell.execute_reply": "2021-05-22T12:30:08.387786Z", + "shell.execute_reply.started": "2021-05-22T10:53:19.935999Z" + }, + "papermill": { + "duration": 2.207532, + "end_time": "2021-05-22T12:30:08.388941", + "exception": false, + "start_time": "2021-05-22T12:30:06.181409", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from aif360.datasets import StandardDataset\n", + "from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric\n", + "import matplotlib.patches as patches\n", + "from aif360.algorithms.preprocessing import Reweighing\n", + "#from packages import *\n", + "#from ml_fairness import *\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "\n", + "from IPython.display import Markdown, display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.063326, + "end_time": "2021-05-22T12:30:08.515911", + "exception": false, + "start_time": "2021-05-22T12:30:08.452585", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "#### Loading the data onto notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:08.648437Z", + "iopub.status.busy": "2021-05-22T12:30:08.647697Z", + "iopub.status.idle": "2021-05-22T12:30:08.897129Z", + "shell.execute_reply": "2021-05-22T12:30:08.897736Z", + "shell.execute_reply.started": "2021-05-22T10:54:54.911570Z" + }, + "papermill": { + "duration": 0.318347, + "end_time": "2021-05-22T12:30:08.897907", + "exception": false, + "start_time": "2021-05-22T12:30:08.579560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ageworkclassfnlwgteducationeducation.nummarital.statusoccupationrelationshipracesexcapital.gaincapital.losshours.per.weeknative.countryincome
090?77053HS-grad9Widowed?Not-in-familyWhiteFemale0435640United-States<=50K
182Private132870HS-grad9WidowedExec-managerialNot-in-familyWhiteFemale0435618United-States<=50K
266?186061Some-college10Widowed?UnmarriedBlackFemale0435640United-States<=50K
354Private1403597th-8th4DivorcedMachine-op-inspctUnmarriedWhiteFemale0390040United-States<=50K
441Private264663Some-college10SeparatedProf-specialtyOwn-childWhiteFemale0390040United-States<=50K
................................................
3255622Private310152Some-college10Never-marriedProtective-servNot-in-familyWhiteMale0040United-States<=50K
3255727Private257302Assoc-acdm12Married-civ-spouseTech-supportWifeWhiteFemale0038United-States<=50K
3255840Private154374HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States>50K
3255958Private151910HS-grad9WidowedAdm-clericalUnmarriedWhiteFemale0040United-States<=50K
3256022Private201490HS-grad9Never-marriedAdm-clericalOwn-childWhiteMale0020United-States<=50K
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" + ], + "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", + "32556 22 Private 310152 Some-college 10 Never-married \n", + "32557 27 Private 257302 Assoc-acdm 12 Married-civ-spouse \n", + "32558 40 Private 154374 HS-grad 9 Married-civ-spouse \n", + "32559 58 Private 151910 HS-grad 9 Widowed \n", + "32560 22 Private 201490 HS-grad 9 Never-married \n", + "\n", + " occupation relationship race sex capital.gain \\\n", + "0 ? Not-in-family White Female 0 \n", + "1 Exec-managerial Not-in-family White Female 0 \n", + "2 ? Unmarried Black Female 0 \n", + "3 Machine-op-inspct Unmarried White Female 0 \n", + "4 Prof-specialty Own-child White Female 0 \n", + "... ... ... ... ... ... \n", + "32556 Protective-serv Not-in-family White Male 0 \n", + "32557 Tech-support Wife White Female 0 \n", + "32558 Machine-op-inspct Husband White Male 0 \n", + "32559 Adm-clerical Unmarried White Female 0 \n", + "32560 Adm-clerical Own-child White Male 0 \n", + "\n", + " capital.loss hours.per.week native.country income \n", + "0 4356 40 United-States <=50K \n", + "1 4356 18 United-States <=50K \n", + "2 4356 40 United-States <=50K \n", + "3 3900 40 United-States <=50K \n", + "4 3900 40 United-States <=50K \n", + "... ... ... ... ... \n", + "32556 0 40 United-States <=50K \n", + "32557 0 38 United-States <=50K \n", + "32558 0 40 United-States >50K \n", + "32559 0 40 United-States <=50K \n", + "32560 0 20 United-States <=50K \n", + "\n", + "[32561 rows x 15 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"../../Data/adult.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.063941, + "end_time": "2021-05-22T12:30:09.027478", + "exception": false, + "start_time": "2021-05-22T12:30:08.963537", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "![](https://kiarofoods.com/wp-content/uploads/2019/10/line_break.png)\n", + "# Exploratory Data Analysis:\n", + "**Problem Type Identification:** We have the target variable available with us. So, it a supervised machine learning problem. First we try to find out the type of supervised machine learning that we have in this case study by lookin at the target variable" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:09.160143Z", + "iopub.status.busy": "2021-05-22T12:30:09.159494Z", + "iopub.status.idle": "2021-05-22T12:30:09.177163Z", + "shell.execute_reply": "2021-05-22T12:30:09.176380Z", + "shell.execute_reply.started": "2021-05-22T10:57:23.920570Z" + }, + "papermill": { + "duration": 0.085203, + "end_time": "2021-05-22T12:30:09.177368", + "exception": false, + "start_time": "2021-05-22T12:30:09.092165", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Target: 'Income'\n", + "Unique Values in Income: ['<=50K' '>50K']\n", + "Number of unique values: 2\n" + ] + } + ], + "source": [ + "print(f\"Target: 'Income'\\nUnique Values in Income: {data.income.unique()}\\nNumber of unique values: {data.income.nunique()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.064101, + "end_time": "2021-05-22T12:30:09.308247", + "exception": false, + "start_time": "2021-05-22T12:30:09.244146", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In the problem, we have 'Income' as the Target variable. we see that we have only two values which are to be predicted, either the income is greater than 50K, which is Yes, or the income is less than or equal to 50K, which is No. We will label encode the target variable." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:09.455717Z", + "iopub.status.busy": "2021-05-22T12:30:09.450535Z", + "iopub.status.idle": "2021-05-22T12:30:09.508216Z", + "shell.execute_reply": "2021-05-22T12:30:09.507485Z", + "shell.execute_reply.started": "2021-05-22T10:59:13.993039Z" + }, + "papermill": { + "duration": 0.134902, + "end_time": "2021-05-22T12:30:09.508369", + "exception": false, + "start_time": "2021-05-22T12:30:09.373467", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data['income'] = data['income'].str.replace('<=50K', '0')\n", + "data['income'] = data['income'].str.replace('>50K', '1')\n", + "data['income'] = data['income'].astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:09.643328Z", + "iopub.status.busy": "2021-05-22T12:30:09.642629Z", + "iopub.status.idle": "2021-05-22T12:30:09.646984Z", + "shell.execute_reply": "2021-05-22T12:30:09.646401Z", + "shell.execute_reply.started": "2021-05-22T10:59:47.496308Z" + }, + "papermill": { + "duration": 0.07423, + "end_time": "2021-05-22T12:30:09.647177", + "exception": false, + "start_time": "2021-05-22T12:30:09.572947", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.income.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.065668, + "end_time": "2021-05-22T12:30:09.777597", + "exception": false, + "start_time": "2021-05-22T12:30:09.711929", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can see that, we have encoded the values of the target variable, and converted it into int data-type. This problem is a classification problem with 'Income' as the target variable. Making a copy of the dataset to work ahead" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:09.914273Z", + "iopub.status.busy": "2021-05-22T12:30:09.913208Z", + "iopub.status.idle": "2021-05-22T12:30:09.930876Z", + "shell.execute_reply": "2021-05-22T12:30:09.930375Z", + "shell.execute_reply.started": "2021-05-22T11:17:31.947406Z" + }, + "papermill": { + "duration": 0.087218, + "end_time": "2021-05-22T12:30:09.931039", + "exception": false, + "start_time": "2021-05-22T12:30:09.843821", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unique values in 'education': 16\n", + "Unique values in 'Education_num': 16\n" + ] + } + ], + "source": [ + "ds = data.copy()\n", + "print(f\"Unique values in 'education': {ds.education.nunique()}\\nUnique values in 'Education_num': {ds['education.num'].nunique()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.065555, + "end_time": "2021-05-22T12:30:10.061681", + "exception": false, + "start_time": "2021-05-22T12:30:09.996126", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We see that for the feature 'Education', we already have the encoded values in feature 'Education_num'. 'Education' will be removed from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:10.199797Z", + "iopub.status.busy": "2021-05-22T12:30:10.198623Z", + "iopub.status.idle": "2021-05-22T12:30:10.207946Z", + "shell.execute_reply": "2021-05-22T12:30:10.207407Z", + "shell.execute_reply.started": "2021-05-22T11:17:34.600530Z" + }, + "papermill": { + "duration": 0.080077, + "end_time": "2021-05-22T12:30:10.208151", + "exception": false, + "start_time": "2021-05-22T12:30:10.128074", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds.drop(['education'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.072381, + "end_time": "2021-05-22T12:30:10.363206", + "exception": false, + "start_time": "2021-05-22T12:30:10.290825", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Checking to see that is there any Null values present in the data that we have. Handling the null values will be the first thing that we need to do. Then, we have a look at the data-types of the other features and the value counts and unique values in those features." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:10.513390Z", + "iopub.status.busy": "2021-05-22T12:30:10.512671Z", + "iopub.status.idle": "2021-05-22T12:30:11.349886Z", + "shell.execute_reply": "2021-05-22T12:30:11.348807Z", + "shell.execute_reply.started": "2021-05-22T11:02:17.186379Z" + }, + "papermill": { + "duration": 0.921945, + "end_time": "2021-05-22T12:30:11.350081", + "exception": false, + "start_time": "2021-05-22T12:30:10.428136", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Null values in the data\", fontsize = 12)\n", + "sns.heatmap(ds.isnull(), cmap = 'inferno')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.066195, + "end_time": "2021-05-22T12:30:11.484323", + "exception": false, + "start_time": "2021-05-22T12:30:11.418128", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "From the heatmap, we see that the dataset consists of no null values. But for some features, we have '?' as the values present. **'?' will be considered as null values.** We move ahead with the feature engineering part. Checking the datatypes of the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:11.624766Z", + "iopub.status.busy": "2021-05-22T12:30:11.624073Z", + "iopub.status.idle": "2021-05-22T12:30:11.630244Z", + "shell.execute_reply": "2021-05-22T12:30:11.629349Z", + "shell.execute_reply.started": "2021-05-22T11:17:40.866104Z" + }, + "papermill": { + "duration": 0.079565, + "end_time": "2021-05-22T12:30:11.630434", + "exception": false, + "start_time": "2021-05-22T12:30:11.550869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datatype of every feature: \n" + ] + }, + { + "data": { + "text/plain": [ + "age int64\n", + "workclass object\n", + "fnlwgt int64\n", + "education.num int64\n", + "marital.status object\n", + "occupation object\n", + "relationship object\n", + "race object\n", + "sex object\n", + "capital.gain int64\n", + "capital.loss int64\n", + "hours.per.week int64\n", + "native.country object\n", + "income int64\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"Datatype of every feature: \")\n", + "ds.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:11.769528Z", + "iopub.status.busy": "2021-05-22T12:30:11.768822Z", + "iopub.status.idle": "2021-05-22T12:30:11.825548Z", + "shell.execute_reply": "2021-05-22T12:30:11.824990Z", + "shell.execute_reply.started": "2021-05-22T11:03:44.817299Z" + }, + "papermill": { + "duration": 0.127978, + "end_time": "2021-05-22T12:30:11.825697", + "exception": false, + "start_time": "2021-05-22T12:30:11.697719", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique values in every feature: \n" + ] + }, + { + "data": { + "text/plain": [ + "age 73\n", + "workclass 9\n", + "fnlwgt 21648\n", + "education.num 16\n", + "marital.status 7\n", + "occupation 15\n", + "relationship 6\n", + "race 5\n", + "sex 2\n", + "capital.gain 119\n", + "capital.loss 92\n", + "hours.per.week 94\n", + "native.country 42\n", + "income 2\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"Number of unique values in every feature: \")\n", + "ds.nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.066997, + "end_time": "2021-05-22T12:30:11.960112", + "exception": false, + "start_time": "2021-05-22T12:30:11.893115", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "*'Workclass', 'Marital_status', 'Occupation', 'Relationship', 'Race', 'Sex', 'Native_country' are the categorical variables in the data*. Proper encoding or conversion of these variables is necessary for the feature engineering. We will look at these attributes and convert them one by one.\n", + "\n", + "**'Workclass':** Starting off with the work class, we look the number of unique values and value counts for those values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:12.111061Z", + "iopub.status.busy": "2021-05-22T12:30:12.110406Z", + "iopub.status.idle": "2021-05-22T12:30:12.114569Z", + "shell.execute_reply": "2021-05-22T12:30:12.113921Z", + "shell.execute_reply.started": "2021-05-22T11:04:39.967259Z" + }, + "papermill": { + "duration": 0.087186, + "end_time": "2021-05-22T12:30:12.114711", + "exception": false, + "start_time": "2021-05-22T12:30:12.027525", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Private 22696\n", + "Self-emp-not-inc 2541\n", + "Local-gov 2093\n", + "? 1836\n", + "State-gov 1298\n", + "Self-emp-inc 1116\n", + "Federal-gov 960\n", + "Without-pay 14\n", + "Never-worked 7\n", + "Name: workclass, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.workclass.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.067424, + "end_time": "2021-05-22T12:30:12.251622", + "exception": false, + "start_time": "2021-05-22T12:30:12.184198", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "In work class, *majority of the people are private employees*. The *minority of people are either working without-pay or they have never-worked*. We can combine the values of these two values as one. first we remove the blank space from the column is present in any values." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:12.418350Z", + "iopub.status.busy": "2021-05-22T12:30:12.408434Z", + "iopub.status.idle": "2021-05-22T12:30:12.421536Z", + "shell.execute_reply": "2021-05-22T12:30:12.420858Z", + "shell.execute_reply.started": "2021-05-22T11:17:46.040567Z" + }, + "papermill": { + "duration": 0.102325, + "end_time": "2021-05-22T12:30:12.421692", + "exception": false, + "start_time": "2021-05-22T12:30:12.319367", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds['workclass'] = ds['workclass'].str.replace('Never-worked', 'Without-pay')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.06746, + "end_time": "2021-05-22T12:30:12.556900", + "exception": false, + "start_time": "2021-05-22T12:30:12.489440", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now, we have 8 unique values in this feature. But, we see that **there are some values where we have '?' in the column. This values can be replaced with NaN values.**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:12.700485Z", + "iopub.status.busy": "2021-05-22T12:30:12.699627Z", + "iopub.status.idle": "2021-05-22T12:30:12.703246Z", + "shell.execute_reply": "2021-05-22T12:30:12.703708Z", + "shell.execute_reply.started": "2021-05-22T11:17:47.541260Z" + }, + "papermill": { + "duration": 0.079086, + "end_time": "2021-05-22T12:30:12.703893", + "exception": false, + "start_time": "2021-05-22T12:30:12.624807", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds['workclass'] = ds['workclass'].replace('?', np.NaN)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:12.890549Z", + "iopub.status.busy": "2021-05-22T12:30:12.863584Z", + "iopub.status.idle": "2021-05-22T12:30:13.135451Z", + "shell.execute_reply": "2021-05-22T12:30:13.135902Z", + "shell.execute_reply.started": "2021-05-22T11:06:49.717904Z" + }, + "papermill": { + "duration": 0.364176, + "end_time": "2021-05-22T12:30:13.136099", + "exception": false, + "start_time": "2021-05-22T12:30:12.771923", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10,6))\n", + "plt.title(\"Income of people according to their workclass\", fontsize = 16)\n", + "sns.countplot(y = ds['workclass'], hue = ds['income'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.068389, + "end_time": "2021-05-22T12:30:13.272977", + "exception": false, + "start_time": "2021-05-22T12:30:13.204588", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We see that the **majority of people who have income more than 50K a year are from private sector**. Same goes for the people with income less than 50K. But *for the Self Employed sector, the number of people whose income > 50K are more than the number of people whose income < 50K.* Now, moving ahead with replacing the null values and encoding the feature. **We will replace the NaN values in the 'Workclass' feature by the mode of the column, grouping it by the 'Occupation' feature.** We now have 7 unique values in Workclass feature. We can encode these values using the frequency encoding technique." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:13.414564Z", + "iopub.status.busy": "2021-05-22T12:30:13.413527Z", + "iopub.status.idle": "2021-05-22T12:30:13.495413Z", + "shell.execute_reply": "2021-05-22T12:30:13.494716Z", + "shell.execute_reply.started": "2021-05-22T11:17:50.411592Z" + }, + "papermill": { + "duration": 0.153793, + "end_time": "2021-05-22T12:30:13.495561", + "exception": false, + "start_time": "2021-05-22T12:30:13.341768", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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occupation?Adm-clericalArmed-ForcesCraft-repairExec-managerialFarming-fishingHandlers-cleanersMachine-op-inspctOther-servicePriv-house-servProf-specialtyProtective-servSalesTech-supportTransport-moving
workclassWithout-payPrivateFederal-govPrivatePrivatePrivatePrivatePrivatePrivatePrivatePrivateLocal-govPrivatePrivatePrivate
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" + ], + "text/plain": [ + "occupation ? Adm-clerical Armed-Forces Craft-repair \\\n", + "workclass Without-pay Private Federal-gov Private \n", + "\n", + "occupation Exec-managerial Farming-fishing Handlers-cleaners \\\n", + "workclass Private Private Private \n", + "\n", + "occupation Machine-op-inspct Other-service Priv-house-serv Prof-specialty \\\n", + "workclass Private Private Private Private \n", + "\n", + "occupation Protective-serv Sales Tech-support Transport-moving \n", + "workclass Local-gov Private Private Private " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import mode\n", + "workclass_mode = ds.pivot_table(values='workclass', columns='occupation',aggfunc=(lambda x:mode(x).mode[0]))\n", + "workclass_mode" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:13.650551Z", + "iopub.status.busy": "2021-05-22T12:30:13.644849Z", + "iopub.status.idle": "2021-05-22T12:30:13.833796Z", + "shell.execute_reply": "2021-05-22T12:30:13.833213Z", + "shell.execute_reply.started": "2021-05-22T11:17:55.728405Z" + }, + "papermill": { + "duration": 0.267648, + "end_time": "2021-05-22T12:30:13.833942", + "exception": false, + "start_time": "2021-05-22T12:30:13.566294", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "loc1 = ds['workclass'].isnull()\n", + "ds.loc[loc1, 'workclass'] = ds.loc[loc1,'occupation'].apply(lambda x: workclass_mode[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:13.980700Z", + "iopub.status.busy": "2021-05-22T12:30:13.979664Z", + "iopub.status.idle": "2021-05-22T12:30:14.174549Z", + "shell.execute_reply": "2021-05-22T12:30:14.173961Z", + "shell.execute_reply.started": "2021-05-22T11:17:57.327677Z" + }, + "papermill": { + "duration": 0.271639, + "end_time": "2021-05-22T12:30:14.174691", + "exception": false, + "start_time": "2021-05-22T12:30:13.903052", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "workclass\n", + "Federal-gov 0.029483\n", + "Local-gov 0.064279\n", + "Private 0.697030\n", + "Self-emp-inc 0.034274\n", + "Self-emp-not-inc 0.078038\n", + "State-gov 0.039864\n", + "Without-pay 0.057031\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "0 0.057031\n", + "1 0.697030\n", + "2 0.057031\n", + "Name: workclass_enc, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "workclass_enc = (ds.groupby('workclass').size()) / len(ds)\n", + "print(workclass_enc)\n", + "\n", + "ds['workclass_enc'] = ds['workclass'].apply(lambda x : workclass_enc[x])\n", + "ds['workclass_enc'].head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:14.324434Z", + "iopub.status.busy": "2021-05-22T12:30:14.323727Z", + "iopub.status.idle": "2021-05-22T12:30:14.326590Z", + "shell.execute_reply": "2021-05-22T12:30:14.327070Z", + "shell.execute_reply.started": "2021-05-22T11:18:00.236320Z" + }, + "papermill": { + "duration": 0.082095, + "end_time": "2021-05-22T12:30:14.327253", + "exception": false, + "start_time": "2021-05-22T12:30:14.245158", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds.drop(['workclass'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.069931, + "end_time": "2021-05-22T12:30:14.468863", + "exception": false, + "start_time": "2021-05-22T12:30:14.398932", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**'Occupation':** Similar to 'Workclass', we will look at the unique values and value counts in the 'Occupation' feature." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:14.621611Z", + "iopub.status.busy": "2021-05-22T12:30:14.620840Z", + "iopub.status.idle": "2021-05-22T12:30:14.627363Z", + "shell.execute_reply": "2021-05-22T12:30:14.626801Z", + "shell.execute_reply.started": "2021-05-22T11:10:37.021352Z" + }, + "papermill": { + "duration": 0.088877, + "end_time": "2021-05-22T12:30:14.627506", + "exception": false, + "start_time": "2021-05-22T12:30:14.538629", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Prof-specialty 4140\n", + "Craft-repair 4099\n", + "Exec-managerial 4066\n", + "Adm-clerical 3770\n", + "Sales 3650\n", + "Other-service 3295\n", + "Machine-op-inspct 2002\n", + "? 1843\n", + "Transport-moving 1597\n", + "Handlers-cleaners 1370\n", + "Farming-fishing 994\n", + "Tech-support 928\n", + "Protective-serv 649\n", + "Priv-house-serv 149\n", + "Armed-Forces 9\n", + "Name: occupation, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.occupation.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.069653, + "end_time": "2021-05-22T12:30:14.767616", + "exception": false, + "start_time": "2021-05-22T12:30:14.697963", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**We will drop the rows where the occupation is NaN.**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:14.915915Z", + "iopub.status.busy": "2021-05-22T12:30:14.915269Z", + "iopub.status.idle": "2021-05-22T12:30:14.946796Z", + "shell.execute_reply": "2021-05-22T12:30:14.946255Z", + "shell.execute_reply.started": "2021-05-22T11:18:05.882254Z" + }, + "papermill": { + "duration": 0.107727, + "end_time": "2021-05-22T12:30:14.946943", + "exception": false, + "start_time": "2021-05-22T12:30:14.839216", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agefnlwgteducation.nummarital.statusoccupationrelationshipracesexcapital.gaincapital.losshours.per.weeknative.countryincomeworkclass_enc
1821328709WidowedExec-managerialNot-in-familyWhiteFemale0435618United-States00.69703
3541403594DivorcedMachine-op-inspctUnmarriedWhiteFemale0390040United-States00.69703
44126466310SeparatedProf-specialtyOwn-childWhiteFemale0390040United-States00.69703
5342168649DivorcedOther-serviceUnmarriedWhiteFemale0377045United-States00.69703
6381506016SeparatedAdm-clericalUnmarriedWhiteMale0377040United-States00.69703
.............................................
325562231015210Never-marriedProtective-servNot-in-familyWhiteMale0040United-States00.69703
325572725730212Married-civ-spouseTech-supportWifeWhiteFemale0038United-States00.69703
32558401543749Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States10.69703
32559581519109WidowedAdm-clericalUnmarriedWhiteFemale0040United-States00.69703
32560222014909Never-marriedAdm-clericalOwn-childWhiteMale0020United-States00.69703
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30718 rows × 14 columns

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" + ], + "text/plain": [ + " age fnlwgt education.num marital.status occupation \\\n", + "1 82 132870 9 Widowed Exec-managerial \n", + "3 54 140359 4 Divorced Machine-op-inspct \n", + "4 41 264663 10 Separated Prof-specialty \n", + "5 34 216864 9 Divorced Other-service \n", + "6 38 150601 6 Separated Adm-clerical \n", + "... ... ... ... ... ... \n", + "32556 22 310152 10 Never-married Protective-serv \n", + "32557 27 257302 12 Married-civ-spouse Tech-support \n", + "32558 40 154374 9 Married-civ-spouse Machine-op-inspct \n", + "32559 58 151910 9 Widowed Adm-clerical \n", + "32560 22 201490 9 Never-married Adm-clerical \n", + "\n", + " relationship race sex capital.gain capital.loss \\\n", + "1 Not-in-family White Female 0 4356 \n", + "3 Unmarried White Female 0 3900 \n", + "4 Own-child White Female 0 3900 \n", + "5 Unmarried White Female 0 3770 \n", + "6 Unmarried White Male 0 3770 \n", + "... ... ... ... ... ... \n", + "32556 Not-in-family White Male 0 0 \n", + "32557 Wife White Female 0 0 \n", + "32558 Husband White Male 0 0 \n", + "32559 Unmarried White Female 0 0 \n", + "32560 Own-child White Male 0 0 \n", + "\n", + " hours.per.week native.country income workclass_enc \n", + "1 18 United-States 0 0.69703 \n", + "3 40 United-States 0 0.69703 \n", + "4 40 United-States 0 0.69703 \n", + "5 45 United-States 0 0.69703 \n", + "6 40 United-States 0 0.69703 \n", + "... ... ... ... ... \n", + "32556 40 United-States 0 0.69703 \n", + "32557 38 United-States 0 0.69703 \n", + "32558 40 United-States 1 0.69703 \n", + "32559 40 United-States 0 0.69703 \n", + "32560 20 United-States 0 0.69703 \n", + "\n", + "[30718 rows x 14 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds['occupation'] = ds['occupation'].replace('?', np.NaN)\n", + "ds = ds.loc[ds['occupation'].isnull() == False]\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.071739, + "end_time": "2021-05-22T12:30:15.089944", + "exception": false, + "start_time": "2021-05-22T12:30:15.018205", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we cannn see that after removing the null values from 'occupation', we are left with 30718 observations." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:15.254263Z", + "iopub.status.busy": "2021-05-22T12:30:15.253499Z", + "iopub.status.idle": "2021-05-22T12:30:15.582177Z", + "shell.execute_reply": "2021-05-22T12:30:15.581628Z", + "shell.execute_reply.started": "2021-05-22T11:12:57.167856Z" + }, + "papermill": { + "duration": 0.421429, + "end_time": "2021-05-22T12:30:15.582322", + "exception": false, + "start_time": "2021-05-22T12:30:15.160893", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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7HB0d+eyzzwptX7hwIfn5+eX2wPMHH3yQ7du307x5c0JCQor8udYje7p06cK3337LhQsXrNsuXLjAsmXLbii+DRs2FJrmcOHCBf773//Svn174HJR6O/vT1JSUrExt2jRAih7Dr/99lsuXrxofX348GE2bNhg7bc4Dz74IHv27MHPz6/YWNzc3Eo8Nysrq8ivxufNm0dBQUGhbd27d+ebb76xjlBerXHjxvj7+zNr1qwSV9V37twZoEguPv30U4Drvl9169alXr16LFq0qND2L774gvz8/GueW1pXHoP0+eefF9r+1VdflaqPK/fq1Z+pLl26YLFYirT76aef4ujoeM33t6Q2S6u092p5KM3nsTT3Snh4OGaz+ZpP0qhfvz5AkScp/Pe//y32+Ot9pnNycigoKCjym4w//ufuCicnp1K9HzfyvsvtRSOwIreRt99+my5dutC+fXuGDRtG3bp1OXjwIFu3buXdd9/F09OTYcOGMXnyZFxdXenRowe7d+9m9OjRdOzYsch8rT/rjTfeIDQ0lM6dOzNo0CD8/f05e/YsO3fu5ODBg8yePbvEc8eMGcM333xDt27dGDlyJCaTiSlTppCVlcW//vWvPx1TzZo16d69O+PGjcPJyYkpU6Zw8eJFxowZA1wekYqPj+fRRx8lNzeX3r174+3tzalTp0hMTMTPz49XX321zDmsUqUK3bt3JyoqipycHMaOHYu7uztDhw4tMdahQ4eycOFCOnXqxNChQ2nSpAkXL15kz549rFmzhq+//rrEcx988EG++uorhg4dSs+ePdm8eTPvvvtuoTmMAOPHj+fbb78lLCyM119/nYYNG3LixAm+//575s+fj8lkYvr06Tz++OPcd999vPLKK9SoUYPdu3eTmprK+PHjCQoK4umnn2bcuHHk5+cTFhbG+vXrmTBhAk8//bR17mZJzGYzY8eOpX///rz44os89dRT7N+/n5iYmBKnmJTVlT5efvll+vfvz5NPPsnBgweJiYmhWrVqJc7ZvOLKc1Tj4+N54YUXcHBwoEWLFjz00EN07NiRV155hdOnT9O8eXO+/fZbZs2aRXR09DWnJzRu3Bh7e3tmz56Np6en9VFM1/qPyR+V9l4tD6X5PJbmXgkICGDo0KFMnTqVCxcu8Mgjj2BnZ8fGjRsJDAykT58+1K5dmy5dujB58mS8vb3x8fFh/vz5JX7hw/U+09WqVaNdu3bExcVRu3ZtvL29mT17drEjxc2aNWPNmjV888031KpVC29vb/z9/YscdyPvu9xmKngRmUilU9JTCDp06FDk2Pr16xdZRb1lyxajZ8+eRrVq1QxnZ2ejSZMmRkxMjHW/xWIxpk6dajRu3NhwcHAwatWqZQwcOND6GJ8rAGPUqFHXja2k+I4dO2ZEREQYvr6+1n7Cw8ONefPmXTcHGzZsMLp162a4uroaLi4uxn333Wf88ssvhY4p61MInn32WeOjjz4yGjRoYDg6OhrBwcHGihUrihybmJhoPPzww0b16tUNJycno379+kafPn2MxMRE6zFlyeHrr79uTJo0yahTp47h5ORkdOzY0fjtt98KHXf1SnHDMIz09HRjyJAhhr+/v+Hg4GDUqFHD6NixozFt2rRrXmtBQYExatQoo3bt2kaVKlWMzp07G1u2bCn2Xtm/f7/x1FNPGV5eXoaTk5PRoEGDIqvEV6xYYXTt2tVwdXU1XF1djRYtWhizZ8+27s/JyTFGjRpl+Pn5Gfb29oafn58xatQoIzc313rMlacQfPTRR8XGPH36dMPPz89wcnIyWrdubaxZs6ZIvCWtRC9u5TjFPBlh2rRphfr4+eefjerVqxtDhgy5Zj4NwzDGjRtn+Pr6GmazuVAM58+fNyIjI41atWoZDg4ORqNGjYypU6eWaoX5Bx98YNx1112GnZ1doZX3Zbmm0tyrJT2FoLgnIJSkNJ9Hw7j+vWIYhvH+++8bd999t+Ho6Gh4eHgYXbp0KRTvsWPHrH9/1axZ04iOji72s17az/ShQ4eMBx980KhatapRo0YNIzIy0vjmm2+KPO1g9+7dRseOHY0qVaoUejrF1U8hMIzSve8l5bm4+1gqhskwrvHEZhGR24C/v791esWtdOWLDCZOnHhL+5Xr27x5M23atOGTTz6hb9++FR2OlFFFfaal8tAUAhERua0dOnSI+Ph4OnXqhLu7O7t37+bNN9/krrvu4q9//WtFhyciFUAFrIiI3NaqVKnCzp07+eSTTzh79iweHh6Eh4cTExNT4jOTRaRy0xQCEREREbEpeoyWiIiIiNgUFbAiIiIiYlNUwIqIiIiITdEirjvMyZMnKzoEm+bt7X1DX18plymPN045LB/K441TDsuH8ljUtb62XAXsHSY5qn9Fh2DTiv/STikr5fHGKYfl40bzWDt2VrnEISJloykEIiIiImJTVMCKiIiIiE3RFAIRERGRSsQwDLKzs7FYLJhMpooO55oMw8BsNuPs7FymWFXAioiIiFQi2dnZODg4YG9vG2Vefn4+2dnZVKlSpdTnaAqBiIiISCVisVhspngFsLe3x2KxlO2cmxRLmfTp0wc/Pz/r6w4dOtCrV6+KC+g2M3r0aCZOnHjNY/r27cu8efNuUUQiIiJyu7rdpw0Up6wx3xYFrKOjI7GxsRUdxm2noKAAOzu76xavIiIiItfyyCOPsHTp0ooOo9zcFgVscbKysoiOjmbkyJH4+voyffp0goKCCA8PZ+nSpaxfv568vDxCQ0Pp3bs3AKtXr2bZsmWYTCb8/PwYPHhwkXb79u1L9+7d+e233/Dw8ODpp59m/vz5pKWl0a9fP0JCQkhNTeW9994jJycHgJdeeokmTZqQlJTE559/jpubG8eOHaNBgwYMHjwYk8nE4sWL+fXXX8nNzaVx48YMGDAAk8nE/v37+eCDDzCZTLRo0YKtW7cSFxeHxWLh008/ZdeuXeTl5fHAAw9w//33k5SUxMKFC3F1deXkyZPMmDHDOrqanZ3NW2+9xcWLF8nPz+epp56iTZs2t/R9EREREdtTmYpXuE0K2NzcXKKioqyvH3vsMcLCwoiIiCA+Pp4ePXpw8eJFwsPD2bZtG8nJybz55psYhsFbb73Frl27cHNzY8mSJUyYMAF3d3cyMzOL7SsnJ4egoCD69u1LbGwsn332GaNHj+b48ePEx8cTEhJCtWrVGD16NI6OjiQnJzNjxgxiYmIAOHToEFOnTsXDw4MxY8awd+9eAgMDefDBB3niiScAePfdd/n1118JCQnh/fff529/+xuNGzfm008/tcaxcuVKXFxcmDx5Mnl5eYwZM4aWLVta+4iLi8PHx6dQ7A4ODgwfPhwXFxcyMjIYNWoUISEh1xx2T0hIICEhAYCYmBiigyL+xDskImLb5r7Qvtjt+vYjuVM0atSIffv2kZiYaK1j9u7dS4sWLXj33XcxmUxs3bqVf/3rX2RlZeHk5MTChQuxt7cnOjqa7du3Y2dnx9ixY+nQoQMLFy7khx9+ICsri0OHDvHKK6+Qm5vLF198gaOjI/PmzcPDw4PDhw8zatQozpw5Q5UqVYiNjaVhw4Y3fD23RQFb0hSCFi1asH79ej7++GPr/m3btrF9+3ZGjBgBXF5pl5KSwpEjR2jXrh3u7u4AVK1atdi+7O3tCQ4OBsDPz8+6Ss/Pz4/Tp08Dl391//HHH3P48GHMZjPJyf/7rpaGDRvi5eUFgL+/P6mpqQQGBrJz506WLl1KTk4OmZmZ1KtXj6ZNm3Lp0iUaN24MQMeOHdmyZYv1Oo4ePcqGDRuAyyPOycnJ2Nvb07BhwyLFK1x+1MSCBQvYvXs3JpOJ9PR0zp8/T/Xq1UvMbXh4OOHh4SXuFxERkTvLzp07WblyJbVq1eLRRx9l06ZNBAcH8/e//53333+f4OBgLly4gLOzM7NmzcJkMrFixQr279/P008/zZo1awDYu3cvP/zwAzk5OXTo0IHXX3+dH3/8kbFjx7J48WJefvllRowYQUxMDA0aNGDLli1ER0fz+eef3/A13BYFbEksFgsnTpzAycmJixcvWgvHXr16cf/99xc69rvvviv2/JEjRwIQEhJCnz59sLOzs45Ymkwm6yo9s9lMQUEBAN988w3VqlUjNjYWwzB49tlnrW06ODhYfzabzVgsFnJzc/n444+ZPHky3t7eLFq0iNzc3Gtem2EYvPjii9Zi+oqkpCScnJyKPWft2rVkZGQQExODvb09kZGR1+1HRERE5I+Cg4Px9fUFoHnz5hw7dgw3Nzd8fHysdYmbmxsAmzZt4sUXXwQuD+LVrVuXgwcPAhAWFkbVqlWpWrUqbm5u1tqsadOm7Nq1i4sXL/Lrr7/yt7/9zdp3edUtt/VjtP773/9Sp04d/vGPfzBz5kzy8/Np2bIlP/30E9nZ2QDWUcigoCA2bNjAhQsXAMjMzMRsNhMbG0tsbCx9+vQpdb9ZWVl4eHhgNpv5+eefr/toh7y8PADc3d3Jzs7ml19+AcDV1ZUqVaqwb98+ANatW2c9Jzg4mB9//JH8/HwATp48ab2ma8VVrVo17O3t2blzp3XEWERERKS0HB0drT/b2dlZa5EbacdsNlsH4EwmEwUFBVgsFtzd3Vm+fLn1z+rVq28s+P/vthiBvXoObHBwMPfeey8rV67kzTffpEqVKjRt2pQlS5bQu3dvTpw4wahRowBwdnZm8ODB1KtXj8cee4xx48ZhNpvx9/cnMjLyT8XzwAMPEBcXx88//0zLli1LHBG9wtXVlW7dujFs2DCqV69OQECAdd8rr7zCv//9b0wmE82aNcPFxQWA++67j9TUVOsIsbu7e6EcFKdjx45MmTKFYcOGERAQQJ06df7U9YmIiIj8UUBAAKmpqWzdupXg4GAyMzNxdnYmNDSUL7/8ko4dO3LgwAFOnDhBQEAAO3bsuG6bbm5u1KtXj2XLlvGXv/wFwzDYtWsXzZs3v+F4TYZhGDfcipQoOzsbZ2dnAL766ivOnj1rHYqvCN0nf1FhfYuIVBQt4rp5lMPyUZ55zMrKsg6YXfHHRVwffPABn3zyCQCjRo2iRYsW9OnTh61btzJ69Ghr7bJw4ULs7OxKXMS1fft2Jk2aBEDbtm357rvv8PT0LLTv6NGjREdHc+rUKfLz83n00UcZOnRoqWK+Ms2hOCpgb7LExES+/PJLLBYL3t7eREZGWheaVQQVsCJyJ1IBe/Moh+XjZhewt7uyFrC3xRSCyiwsLIywsLCKDkNERESk0ritF3GJiIiIiFxNBayIiIiI2BRNIbjDlDQPTEpHc73Kh/J445RDEbmTaQRWRERERGyKClgRERERsSkqYEVERETkpvjpp5/o1KkTHTp04L333iu3djUH9g6THNW/okOwackVHUAloTzeOFvIYe3YWRUdgoj8QXnXANf7jBcUFDBq1CgWLFhA7dq16dGjB927d6dx48Y33LdGYEVERESk3P3222/4+/tTv359HB0defTRR/nhhx/KpW0VsCIiIiJS7lJSUgp9m1bt2rVJSUkpl7ZVwIqIiIiITVEBKyIiIiLlrlatWpw8edL6Ojk5mVq1apVL2ypgRURERKTcBQcHc+jQIY4ePUpubi5ff/013bt3L5e2b0kB27t3b9555x3r64KCAiIiIoiJiflT7UVGRpKRkVFk++bNm/nqq6/+bJjl6lbFkpqaytq1a296PyIiIiJlYW9vz8SJE3nmmWfo2rUrf/nLX2jSpEn5tF0urVyHk5MTx44dIzc3F0dHR7Zv346np2e59xMSEkJISEi5t/tn3KpYTp8+zdq1a+nYseNN70tERERsV0U82q5bt25069at3Nu9Zc+Bveeee9iyZQvt2rVj3bp1dOjQgT179gCwf/9+5syZQ15eHo6OjgwcOBBfX18sFgvz589n27ZtmEwmunXrxkMPPQTA999/z6+//kp+fj6vvvoqderUYdWqVRw4cICIiAji4+OpUqUKBw8e5Ny5czz33HO0a9cOgKVLl7J+/Xry8vIIDQ2ld+/eReLNzc1l1qxZHDhwADs7O55//nmCgoJYtWoVGzduJCsri/T0dDp16sSTTz5Z5PzSxHL27FmmT59OVlYWFouF/v3707RpU/r27Uu3bt3Yvn071atXZ8iQIbi7u5OSksJHH31ERkYGZrOZoUOH8n//938cP36cqKgounTpQs+ePW/iuygiIiJS8W5ZAduhQwcWL15Mq1atOHLkCPfee6+1gPX19eWNN97Azs6O7du383//938MHz6chIQETp8+zVtvvYWdnR2ZmZnW9tzc3JgyZQo//PADy5Yt45VXXinS57lz53jjjTc4efIkU6ZMoV27dmzbto3k5GTefPNNDMPgrbfeYteuXTRr1qzQuVeeUxYXF8eJEyeYOHEiM2bMAC4X3HFxcTg5OREdHU2rVq0ICAi45vUXF8vatWtp2bIljz/+OBaLhZycHABycnIICAigX79+LF68mM8//5yIiAjeeecdevXqRWhoKLm5uRiGwTPPPMOyZct47bXX/vybIyIiImJDblkBW79+fU6fPs26deu45557Cu3LysoiPj7e+mywgoICALZv30737t2xs7MDoGrVqtZz2rZtC0CDBg3YuHFjsX22adMGs9lM3bp1OX/+PADbtm1j+/btjBgxAoDs7GxSUlKKFLB79uyxjvbWqVOHGjVqkJx8+btvWrRogZubGwChoaHs2bPnugVscbEEBATw/vvvk5+fT2hoKP7+/gCYTCbCwsIA6NSpE2+//TaXLl0iPT2d0NBQABwdHa/Z3xUJCQkkJCQAEBMTQ3RQRKnOqyhzX2hf0SFck7e3N2lpaRUdhs1THm+ccigid7Jb+lWyISEhzJs3j3HjxnHhwgXr9oULF9K8eXOioqJITU1l/Pjx123L3v5y6Gaz2VrwXs3BwcH6s2EY1p979erF/fffX+jY77//nhUrVgAQHR1d+ovicsF5vfOLi6VZs2aMHz+eLVu2EB8fT8+ePenSpUux7f9Z4eHhhIeH/+nzRURERG43t7SAvffee3FxccHPz4+kpCTr9qysLOuirlWrVlm3t2jRguXLl9O8eXPrFII/jsL+GS1btmThwoV06tQJZ2dn0tPTsbOz48EHH+TBBx+0Hte0aVPWrFlDUFAQJ0+eJC0tDV9fXw4dOsSOHTvIzMzE0dGRTZs28fe//52AgIBC55fG6dOn8fLyIjw8nPz8fA4dOkSXLl0wDIMNGzbQoUMH1q5dS2BgIFWqVMHLy4uNGzcSGhpKXl4eFouFKlWqcOnSpRvKiYiIiIgtuaUFrJeXFz169Ciy/dFHHyU+Pp4lS5bQqlUr6/Zu3bqRnJzM8OHDsbe3p1u3bmUuEq/WsmVLTpw4wahRowBwdnZm8ODBVKtWrdBx3bt3Z9asWQwbNgw7OzsGDhxoHUUNCAggLi6OM2fO0KlTp+tOHyhJUlISy5Ytw87ODmdnZwYNGgRcfmrD/v37WbJkCe7u7gwdOhSAQYMG8eGHH7Jo0SLs7Ox49dVX8fPzw2w2axGXiIiI3DFMxh9/ty7X9cenC9wsffv2Zd68eTel7e6Tv7gp7ZYXzYG9MyiPN045LB/K441TDstHeeYxKysLFxeXcmnrRr366qskJCTg7e3NypUrSzyuuJh9fX1LPP6WjsCKiIiISMXo95/15dpeaQadevfuzYsvvsg///nPcu1bBWwZde3ala5du97UPm7W6KuIiIjIrdSuXTuOHTtW7u3ekq+SFREREREpLypgRURERMSmaArBHeZ2XyQlIiIicj0agRURERERm6ICVkRERERuioEDB/LII49w4MABWrduzYIFC8qlXU0hEBEREbkDVMQ0wpkzZ96UdjUCKyIiIiI2RSOwd5jkqP4VHYJNS67oACoJ5fHGKYflwxbzWDt2VkWHIFLhNAIrIiIiIjZFBayIiIhIJWIYRkWHUGZljVkFrIiIiEglYjabyc/Pr+gwSi0/Px+zuWwlqebAioiIiFQizs7OZGdnk5OTg8lkquhwrskwDMxmM87OzmU6r9IUsH369MHPzw+LxUKdOnWIjIzEycmp1OdPnz6d48eP07VrV3r27HkTI/2fzZs3c/z4cXr16lXiMatWreLAgQNERESwceNGfH19qVu37i2JT0RERGyPyWSiSpUqFR3GTVVpphA4OjoSGxtLXFwc9vb2LF++vND+goKCEs89d+4cBw4c4O23375lxStASEjINYvXq23atInjx4/fvIBEREREbEClGYH9o8DAQI4ePUpSUhILFy7E1dWVkydPEhsby6xZszhw4AB2dnY8//zzBAUFMXHiRNLT04mKiuKll16iadOm1rbWr1/P4sWLMZvNuLi4MH78eFatWsXGjRvJysoiPT2dTp068eSTTwLw888/891335Gfn0+jRo3o378/ZrOZrVu3smDBAiwWC25ubvzrX/8qNLq6efNmlixZQn5+Pm5ubgwePJjq1atb49i7dy+bN29m165dfPHFFwwbNoxp06YxZcoUAJKTk5k+fbr1tYiIiEhlVekK2IKCArZu3UpwcDAAhw4dIi4uDh8fH5YtWwZAXFwcJ06cYOLEicyYMYMRI0YwZcoUYmNji7S3ePFiRo0ahaenJxcvXrRu379/P3FxcTg5OREdHU2rVq1wcnIiMTGRCRMmYG9vz6xZs1izZg333HMP//73vxk/fjw+Pj5kZmYW6ScwMJBJkyZhMplYsWIFS5cu5fnnn7fub9KkCSEhIbRu3Zp27doB4OLiwuHDh/H39+enn36ia9eu5ZhJERERkdtTpSlgc3NziYqKAqBp06bcd9997N27l4YNG+Lj4wPAnj17eOihhwCoU6cONWrUIDk5+ZrzRJo0aUJ8fDzt27enbdu21u0tWrTAzc0NgNDQUPbs2YOdnR2HDh0iOjraGpO7uzu///47TZs2tcZRtWrVIv2kp6czffp0zp49S35+vvXYa7nvvvv46aefeOGFF1i/fj1vvvlmkWMSEhJISEgAICYmhuigiOu2KyJyO7mZX3/p7e1NWlraTWtfRG6OSlPAXpkDe7WyLOQCWLBgAVu2bAEgNjaWAQMGsG/fPrZs2cJrr71GTExMseeZTCYMw6BLly4888wzhfZt3rz5uv3Onj2bnj17EhISQlJSEp9//vl1z2nbti2LFy8mKCiIu+66y1pQ/1F4eDjh4eHXbUtERETEVlSaRVyl0bRpU9asWQPAyZMnSUtLw9fXt9AxTz/9NLGxsdZiOCUlhUaNGtGnTx/c3d05c+YMADt27CAzM5Pc3Fw2bdpEkyZNuPvuu9mwYQPnz58HIDMzk9OnT9O4cWN2795NamqqdfvVsrKy8PT0BGD16tXFxl+lShUuXbpkfe3o6EjLli2ZNWsW9957742kRkRERMRmVJoR2NLo3r07s2bNYtiwYdjZ2TFw4EAcHByuec78+fNJTr78bdlBQUHUr1+fw4cPExAQQFxcHGfOnKFTp04EBAQA8NRTTzFx4kQMw8DOzo6IiAgaN27MgAEDePvttzEMA3d3d8aMGVOonyeffJKpU6fi6upKUFCQtdj9o7CwMP7973/z3Xff8eqrr1KrVi06duzIxo0badmyZTllSUREROT2ZjJs8fvGKtgfnx5Q0ZYuXUpWVhZPPfVUqY7vPvmLmxyRiEj50hzY25tyWD6Ux6Ku/i35H91RUwgqm9jYWH7++Wd69OhR0aGIiIiI3DJ31BSC8tK1a9fb4pFVV566ICIiInIn0QisiIiIiNgUFbAiIiIiYlM0heAOczMXQ9wJNMm+fCiPN045FJE7mUZgRURERMSmqIAVEREREZuiAlZEREREbIoKWBERERGxKfomrjvMr8/qSw9ERETuRLVjZ1V0CGWib+ISERERkUpDBayIiIiI2BQVsCIiIiJiU1TAioiIiIhNUQErIiIiIjal0nyV7JkzZ/j44485fvw4hmHQqlUr+vbty/Hjx0lPT6dVq1YALFq0CGdnZx555JEKjrhkmzdv5vjx4/Tq1auiQxERERG57VSKEVjDMHj77bdp06YN77zzDjNmzCA7O5sFCxZw+PBhfvvtt3Lry2KxlEs7BQUFJe4LCQlR8SoiIiJSgkoxArtz504cHR259957ATCbzbzwwgtERkZiZ2eHYRjs2bOHxx57DIDjx48zbtw40tLS6NGjBz16XH426s8//8x3331Hfn4+jRo1on///pjNZvr27cv999/Pjh07iIiIIDAw0Nr3p59+yubNm7Gzs6NFixY8//zzZGRk8OGHH3LmzBkAXnjhBQIDA1m0aBGnTp0iNTUVLy8vTp8+zSuvvEK9evUAGDduHH379uXYsWMcOHCAiIgIzp07x0cffURqaioA/fv3p0mTJiXGKiIiIlLZVYoC9tixY9x1112Ftrm4uFCjRg26du1KcnIyERERwOUpBCdPnmTs2LFcunSJIUOG0L17d1JSUkhMTGTChAnY29sza9Ys1qxZQ5cuXcjJyaFhw4Y8//zzhfq4cOECGzduZPr06ZhMJi5evAjAnDlz6NmzJ4GBgaSlpTFp0iSmTZsGXC6eJ0yYgKOjI9988w3r16+nXr16nD17lrNnzxIQEMCxY8esfcyZM4dmzZoRFRWFxWIhOzub48ePlxjr1RISEkhISAAgJiaG6KCI8kv8/zf3hfbl3ubtytvbm7S0tIoOw+YpjzdOOSwfyuONUw7Lh/JYNpWigC2rVq1a4eDggIODA9WqVeP8+fPs3LmTQ4cOER0dDUBubi7u7u7A5RHddu3aFWnHxcUFR0dH3n//fVq3bk3r1q0B2LFjB8ePH7cel5WVRXZ2NnB5eoCjoyMAYWFhTJw4kd69e7N+/fpi+9i5cyeDBg2yxuHi4sLPP/9cYqxXCw8PJzw8/E/lSUREROR2VCkK2Lp16/LLL78U2paVlUVaWhp2dnZFjre3/99lm81mCgoKMAyDLl268MwzzxQ53sHBwfrr+UmTJnHu3DkCAgJ45ZVXePPNN9mxYwcbNmzg+++/Z+zYsRiGwaRJk6yF6h85OTlZf/b09MTNzY0jR46QmJjIyy+/XKrrvVasIiIiIpVdpZg0effdd5OTk8Pq1auBywutPvnkE7p27Uq1atW4dOlSqdrYsGED58+fByAzM5PTp08XOW7UqFHExsbyyiuvkJ2dTVZWFq1ataJfv34cOXIEgBYtWvD9999bzzl8+HCJ/bZv356vv/6arKws6tevX2xcP/74o/W6srKySh2riIiISGVUKUZgTSYTw4cPZ9asWXzxxRcYhsE999zD008/TU5ODl9//TVRUVHWRVzFqVu3Lk899RQTJ07EMAzs7OyIiIigRo0aJZ5z6dIl3nrrLfLy8jAMwzpH9sUXX+Tjjz9m+PDhFBQU0LRpUwYMGFBsG+3atWPu3Ln89a9/LXZ/v379+PDDD1m5ciVms5mXX36Zxo0blzlWERERkcrCZBiGUdFByK3TffIX5d6mFnFJWSmPN045LB/K441TDsuH8liUr69vifsqxRQCEREREblzqIAVEREREZuiAlZEREREbEqlWMQlpXcnzVcVERGRykkjsCIiIiJiU1TAioiIiIhNUQErIiIiIjZFBayIiIiI2BR9kcEd5tdne1R0CCIiImKjasfOumV96YsMRERERKTSUAErIiIiIjZFBayIiIiI2BQVsCIiIiJiU1TAioiIiIhNqfQF7MaNG+nduzcnTpwodv+4ceM4cOBAufaZmprKsGHDynze6NGj/1R/ixYtYunSpX/qXBERERFbU+kL2HXr1hEYGMi6desqOpQSFRQUADBx4sQKjkRERETk9mdf0QHcTNnZ2ezZs4exY8cyZcoUevfuTW5uLjNnzuTIkSP4+vqSm5trPb5v3750796d3377DQ8PD55++mnmz59PWloa/fr1IyQkpEgfKSkpfPTRR2RkZGA2mxk6dChm8//+X2CxWPj000/ZtWsXeXl5PPDAA9x///0kJSWxcOFCXF1dOXnyJDNmzKBv377MmzcPgK+++oo1a9ZgNpsJDg7m2WefJSEhgRUrVpCfn0/NmjUZPHgwTk5ONz+RIiIiIreRSl3Abtq0ieDgYHx9fXFzc+PgwYPs2rULR0dHpk2bxpEjRxg5cqT1+JycHIKCgujbty+xsbF89tlnjB49muPHjxMfH19sAfvOO+/Qq1cvQkNDyc3NxTAMzp8/b92/cuVKXFxcmDx5Mnl5eYwZM4aWLVsCcOjQIeLi4vDx8SnU5m+//cbmzZt58803cXJyIjMzE4C2bdsSHh4OwGeffcbKlSt56KGHrpmDhIQEEhISAIiJiSE6KOJPZFKuNveF9hUdgk3z9vYmLS2tosOwacph+VAeb5xyWD6Ux7Kp1AXsunXr6NHj8jdPhYWFsXbtWlJSUqzb6tevT/369a3H29vbExwcDICfnx8ODg7Y29vj5+fH6dOni7R/6dIl0tPTCQ0NBcDR0bHIMdu2bePo0aNs2LABgKysLJKTk7G3t6dhw4ZFileAHTt20LVrV+voatWqVQE4duwYn332GRcvXiQ7O9taCF9LeHi4tegVERERqQwqbQGbmZnJzp07OXr0KCaTCYvFAsBdd91V4jl2dnaYTCYATCYT9vaX02M2m63zVGfOnMmhQ4fw9PRkyJAh143DMAxefPFFa2F8RVJSUpl//R8fH09UVBT+/v6sWrWKpKSkMp0vIiIiUhlU2gJ2w4YNdO7cmQEDBli3jR07lgYNGrB27VqCgoI4evQoR44cKVO7AwcOLPTay8uLjRs3EhoaSl5enrVQviI4OJgff/yRoKAg7O3tOXnyJJ6entfso0WLFixevJhOnTpZpxBUrVqV7OxsPDw8yM/PZ82aNddtR0RERKQyqrQF7Lp163j00UcLbWvbti2HDx8mNzeXoUOHUqdOHRo0aHBD/QwaNIgPP/yQRYsWYWdnx6uvvmodxQW47777SE1Ntc61dXd3Jyoq6pptBgcHc/jwYV577TXs7e255557eOaZZ+jTpw+vv/467u7uNGrUiEuXLt1Q7CIiIiK2yGQYhlHRQcit033yFxUdQqWgRVw3RosVbpxyWD6UxxunHJYP5bEoX1/fEvdV+ufAioiIiEjlogJWRERERGyKClgRERERsSkqYEVERETEplTapxBI8bT46MZokr2IiEjF0wisiIiIiNgUFbAiIiIiYlNUwIqIiIiITdEXGdxhfn22R0WHICIiIhWoduysig6hVPRFBiIiIiJSaaiAFRERERGbogJWRERERGyKClgRERERsSkqYEVERETEptxx38R17tw55s6dy4EDB3BxcaF69eq88MIL11zp9kfr169n0aJFVK9enSeeeAJ7e3uaNGlyk6Mu7Mcff8TJyYkuXbrc0n5FREREbgd3VAFrGAaxsbF06dKFIUOGAHD48GHOnz9vLWALCgqws7MrsY2VK1fyt7/9jcDAQBYtWoSzs3OpCliLxYLZXD4D3t27dy92+/ViFxEREakM7qgCNikpCXt7+0IFoL+/P0lJSfzrX//C1dWVkydPMmPGDN566y3OnDlDXl4ePXr0IDw8nMWLF7Nnzx7ef/996tevz+7duzGbzaxZs4aXXnqJpk2bFuqvb9++3H///ezYsYOIiAhSU1P57rvvyM/Pp1GjRvTv3x+z2Uzfvn3p1q0b27dvp3r16gwZMgR3d3cSEhJYsWIF+fn51KxZk8GDB+Pk5GQtnB955BHGjRuHv78/e/bsoUOHDvzlL3+51WkVERERuaXuqAL26NGj3HXXXcXuO3ToEHFxcfj4+AAwcOBAqlatSm5uLtHR0bRt25YnnniCnTt30rdvXwICAgoVksXJycmhYcOGPP/88xw/fpyvvvqKCRMmYG9vz6xZs1izZg1dunQhJyeHgIAA+vXrx+LFi/n888+JiIigbdu2hIeHA/DZZ5+xcuVKHnrooSL95OfnExMTU05ZEhEREbm93VEF7LU0bNjQWrwCfPvtt2zatAmAtLQ0kpOTcXNzK1ObZrOZdu3aAbBz504OHTpEdHQ0ALm5ubi7uwNgMpkICwsDoFOnTrz99tsAHDt2jM8++4yLFy+SnZ1Ny5Yti+3nyrnFSUhIICEhAYCYmBiigyLKdA0ipTH3hfZlOt7b25u0tLSbFM2dQTksH8rjjVMOy4fyWDZ3VAFbr149fvnll2L3OTk5WX9OSkpix44dTJw4EScnJ8aNG0deXt4127ZYLIwcORKAkJAQ+vTpg4ODg3Xeq2EYdOnShWeeeea6cZpMJgDi4+OJiorC39+fVatWkZSUdN3YrxYeHm4dxRURERGpDO6ox2gFBQWRl5dnHZEEOHLkCLt37y50XFZWFq6urjg5OXHixAn27dtXbHtVqlQhOzsbuDzaGhsbS2xsLH369Cly7N13382GDRs4f/48AJmZmZw+fRq4XNxu2LABgLVr1xIYGAhAdnY2Hh4e5Ofns2bNmhu8ehEREZHK4Y4agTWZTAwfPpy5c+fy9ddf4+DgQI0aNWjTpk2h44KDg1m+fDlDhw6ldu3aNGrUqNj2WrduzdSpU9m0aVOxi7j+qG7dujz11FNMnDgRwzCws7MjIiKCGjVq4OTkxP79+1myZAnu7u4MHToUgD59+vD666/j7u5Oo0aNuHTpUvklQ0RERMRGmQzDMMp6ksViKfS6vB4Pdafq27cv8+bNuyV9dZ/8xS3pR+4smgN76ymH5UN5vHHKYflQHou61jP6Sz0Ce/DgQT7++GOOHj1Kbm5uoX0LFy7889GJiIiIiJRBqQvY+Ph4Wrduzd///vdrLhqSsrtVo68iIiIilUGpC9i0tDSefvpp6wp5EREREZGKUOrJq23atGHbtm03MxYRERERkesq9QhsXl4eb7/9NoGBgVSvXr3QvkGDBpV3XHKTlHWxjRSmSfYiIiIVr9QFbN26dalbt+7NjEVERERE5LpKXcA++eSTNzMOEREREZFSKdMXGSQlJbF69WrOnj2Lh4cHnTt3Jigo6GbFJiIiIiJSRKkXca1YsYJp06ZRvXp1QkND8fDwYMaMGYW+llVERERE5GYr9Qjs0qVLGT16NP7+/tZtYWFhxMXFER4efjNik5sgOap/RYdg05IrOoBKwhbzWDt2VkWHICIi/1+pR2AvXLhQZBGXr68vmZmZ5R6UiIiIiEhJSl3ABgYG8sknn5CTkwNAdnY28+bNo3HjxjctOBERERGRq5V6CsHLL7/M9OnT6devH1WrViUzM5PGjRvzz3/+82bGJyIiIiJSSKkLWA8PD8aPH09aWhrnzp3Dw8MDLy+vmxmbiIiIiEgR1yxgDcPAZDIBYLFYAPD09MTT07PQNrO51DMRRERERERuyDUL2H79+vGf//wHgKeffrrE4xYuXFjqDi9cuMAbb7wBwLlz5zCbzbi7uwMwefJk7O3L9Gjam+rw4cOkp6fTqlWrW9bngQMHWL16NS+99NIt61NERETEllyzWoyLi7P+/N5775VLh25ubsTGxgKwaNEinJ2deeSRR6z7CwoKsLOzK5e+bkRBQQGHDx/mwIEDt7SADQgIICAg4Jb1JyIiImJrrlnAent7W39ev359oULzim+++YaePXveUBDx8fE4ODhw+PBhmjRpQocOHZgzZw55eXk4OjoycOBAfH19WbVqFZs3byYnJ4dTp04RGhrKc889h8Vi4f333+fgwYMA3HvvvfTs2ZNx48ZRv359du3ahcVi4e9//zsNGzYkMzOTmTNnkpqaipOTEwMGDKB+/fosWrSIU6dOkZqaipeXF3v37iU3N5c9e/bw2GOPERYWVijuyMhIOnTowG+//YadnR0DBgxgwYIFpKSk8Je//IXu3btjGAbz589n69atAPz1r38lLCyM6dOn07lzZ2txHB8fT+vWrXFzc2PZsmW89tprLFq0iLS0NFJTU0lLS6NHjx706NEDgMWLF7NmzRrc3d3x8vKiQYMGxb4/IiIiIpVNqX9f/8UXXxRbIH3xxRc3XMACpKenM3HiRMxmM1lZWbzxxhvY2dmxfft2/u///o/hw4cDl3+t/9Zbb2Fvb8+QIUN48MEHycjIID093TpifPHiRWu7OTk5xMbGsmvXLt5//33i4uJYtGgRd911FyNGjGDnzp2899571lHh48ePM2HCBBwdHVm1ahUHDhwgIiKixLi9vb2JjY1l7ty5zJw5kwkTJpCXl8ewYcPo3r07v/zyC4cPHyY2NpaMjAyio6Np2rQpYWFhJCYm0qpVK/Lz89m5cycvv/wy+/btK9T+yZMnGTt2LJcuXWLIkCF0796dw4cP88svvxAbG0tBQQEjR46kQYMGxcaXkJBg/ba0mJgYooNKvhZbM/eF9re8T29vb9LS0m55v5WN8igiIjfiugXszp07gcsLtq78fMWpU6eoUqVKuQTSrl0762KwrKws4uPjSUlJAS7/Ov+KoKAgXFxcAKhbty5paWnUrVuX1NRUZs+eTatWrWjRooX1+I4dOwLQrFkzsrKyuHjxInv27GHYsGHW9jIzM8nKygIgJCQER0fHUscdEhICgJ+fH9nZ2VSpUoUqVapgb29v7atDhw6YzWaqV69Os2bNOHDgAMHBwdZR5q1bt9K0adNi+23VqhUODg44ODhQrVo1zp8/z969e2nTpo31+NatW5cYX3h4uL4pTURERCqV6xaw77//PgC5ubnWnwFMJhPVq1cvt8VGzs7O1p8XLlxI8+bNiYqKIjU1lfHjx1v3OTg4WH82m80UFBRQtWpVYmNj2bp1Kz/++COJiYkMHDiw2H6uPFWhJE5OTiXumzRpEufOnSMgIIBXXnkFwLrozGw2FxtbSRwdHWnevDnbtm0jMTGRDh06FHvcHxe1Xa9NERERkTvBdQvY+Ph44PIirkGDBt30gODyCOyVR3WtWrXqusdnZGRgb29Pu3bt8PX15d1337XuS0xMJCgoiD179uDi4oKLiwuBgYGsWbOGJ554gqSkJNzc3Kyjun/k7OzMpUuXrK9HjRpV5mtp2rQpCQkJdO3alczMTHbv3k3fvn0BCAsLY8WKFRw8eJDIyMhSt9mkSRM++ugjevXqhcViYcuWLXTr1q3MsYmIiIjYolLPgb1VxSvAo48+Snx8PEuWLCnVEwDS09N5//33rc+lfeaZZ6z7HB0dGTFiBAUFBfz9738HoHfv3sycOZPhw4fj5ORUYvEYFBTE119/TVRUVLGLuEojNDSU33//naioKACee+45qlevDkCLFi149913adOmTZkeH9awYUNat25NVFQU1apVo169esUW4CIiIiKVkckwDKM0B2ZlZfH555+za9cuLly4wB9P++PUgtvJuHHj6Nu3b6V8LFV2djbOzs7k5OQwduxYBgwYUOJCrj/qPvmLWxDdraFFXLZLebxxymH5UB5vnHJYPpTHonx9fUvcV+phv1mzZpGens4TTzzBu+++y+DBg1m6dClt27YtlyClbP79739z/Phx8vLy6NKlS6mKVxEREZHKoNQF7Pbt25k2bRpubm6YzWbatGlDQEAAU6ZMKZfHaN0M48aNq+gQbpp//vOfFR2CiIiISIUwl/ZAwzCs8yydnZ3JysqievXq1kddiYiIiIjcCqUegb3yjVZ33303gYGBzJo1C2dnZ2rXrn0z45NyVhHzRkVERETKU6lHYP/2t79Ro0YNAF588UUcHBy4ePHiLX06gYiIiIhIqUdga9asaf25WrVq1kdSiYiIiIjcSqV/+CiwcuVK1q1bx9mzZ/Hw8KBDhw7ce++91/12KxERERGR8lLqAnb+/Pls2rSJhx9+2PqssmXLlnHy5Emee+65mxmjiIiIiIhVqQvYVatWMWXKFLy8vKzbWrVqxciRI1XA2pDkqP4VHYJNS67oACoJW81j7dhZFR2CiIhQhkVcVapUoUqVKkW26StMRURERORWKvUIbI8ePXj77bfp1asXnp6enDlzhqVLl/Lwww9z6tQp63F/XOwlIiIiIlLeSl3Azp07F4CkpKRC23fu3MmcOXOsrxcuXFg+kYmIiIiIFKPUBawKUxERERG5HZR6DqyIiIiIyO2g1COw//rXv0p83uv48eOve37fvn2ZN2+e9fWqVas4cOAAERERpQ2hRIsWLcLZ2ZlHHnmE+Ph4WrduTbt27W643etJTU1lypQpxMXF3fS+REREROSyUhew9913X6HX586d46effqJTp07lHtStVFBQgJ2dXUWHUWa2GreIiIjIjSp1Adu1a9ci29q1a8fMmTN54oknbiiIzZs3s2TJEvLz83Fzc2Pw4MFUr16dRYsWkZaWRmpqKmlpafTo0YMePXoAsGTJElavXo27uzteXl40aNCgSLsHDx7kP//5D9nZ2bi7uzNw4EA8PDwYN24c/v7+7Nmzhw4dOuDt7c3ixYsxm824uLgUO6KckpLCRx99REZGBmazmaFDh2I2/28GhsVi4dNPP2XXrl3k5eXxwAMPcP/995Odnc1bb73FxYsXyc/P56mnnqJNmzakpqYyefJkmjRpwu+//46npycjRozA0dGRlJQUPv74YzIyMnBycuJvf/sbderUIT4+HgcHBw4fPkyTJk1o06aNdQGdyWRi/PjxRR51JiIiIlLZlOmrZK/m6enJkSNHSnVsbm4uUVFR1teZmZmEhIQAEBgYyKRJkzCZTKxYsYKlS5fy/PPPA3Dy5EnGjh3LpUuXGDJkCN27d+fo0aOsW7eOt956i4KCAkaOHFmkgM3Pz2f27NmMGDECd3d3EhMTWbBgAQMHDrTuj4mJAWDYsGGMGjUKT09PLl68WGz877zzDr169SI0NJTc3FwMw+D8+fPW/StXrsTFxYXJkyeTl5fHmDFjaNmyJV5eXgwfPhwXFxcyMjIYNWqU9bqTk5P55z//ySuvvMLUqVPZsGEDnTt35sMPP+Tll1+mdu3a7Nu3j1mzZjF27FgA0tPTmThxImazmZiYGCIiIggMDCQ7OxsHB4cicSckJJCQkABATEwM0UFln7Ix94X2ZT6nsrryLXRyY5RHERG5EaUuYFeuXFnodW5uLr/88guNGzcu1fmOjo7ExsZaX1+ZAwuXi7Lp06dz9uxZ8vPz8fHxsR7XqlUrHBwccHBwoFq1apw/f57du3cTGhqKk5MTgLUg/KOTJ09y7NgxJkyYAFweIfXw8LDuDwsLs/7cpEkT4uPjad++PW3bti3S1qVLl0hPTyc0NNR6LVfbtm0bR48eZcOGDQBkZWWRnJyMp6cnCxYsYPfu3ZhMJtLT062Fr4+PD/7+/gA0aNCA06dPk52dzd69e5k6daq17fz8fOvP7dq1s478BgYG8sknn9CxY0fatm1b6FvSrggPDyc8PLzIdhERERFbVeoCds2aNYVeOzk50aRJEx5++OEbDmL27Nn07NmTkJAQkpKS+Pzzz/8XoP3/QjSbzRQUFJS63bp16zJp0qRi910pfgEGDBjAvn372LJlC6+99hoxMTHMmzePQ4cO4enpyZAhQ67bl2EYvPjiiwQHBxfavmrVKjIyMoiJicHe3p7IyEhyc3MBCo2Yms1mcnNzsVgsuLq6Fir2/8jZ2dn6c69evWjVqhVbtmxhzJgxjBo1ijp16lw3VhERERFbVuoC9sqvsG+GrKwsPD09AVi9evV1j2/atCkzZ87kscceo6CggF9//bXIKKOvry8ZGRn8/vvvNG7cmPz8fJKTk6lXr16R9lJSUmjUqBGNGjVi69atnDlzxjrV4AovLy82btxIaGgoeXl5WCyWQvuDg4P58ccfCQoKwt7enpMnT+Lp6UlWVhbVqlXD3t6enTt3cvr06Wtem4uLCz4+Pqxfv5727dtjGAZHjhyxjtReHbefnx9+fn4cOHCAEydOqIAVERGRSq/UBezq1avx9/enfv361m2HDx/m6NGjdO7c+YaCePLJJ5k6dSqurq4EBQWRmpp6zeMbNGhAWFgYUVFRuLu7ExAQUOQYe3t7hg0bxpw5c8jKyqKgoIAePXoUW8DOnz+f5ORkAIKCggpd4xWDBg3iww8/ZNGiRdjZ2fHqq68WeqzYfffdR2pqKiNHjgTA3d2dqKgoOnbsyJQpUxg2bBgBAQGlKjD/8Y9/8NFHH1kXtnXo0KHYAvbbb78lKSkJk8lE3bp1ueeee67btoiIiIitMxmGYZTmwIEDB/LWW29RtWpV67bMzExGjBjBzJkzb1qAUr66T/6izOdoEdf/aPFR+VAeb5xyWD6UxxunHJYP5bEoX1/fEveV+pu4Ll26hIuLS6FtLi4uJa7aFxERERG5GUpdwNatW9e6wv6KjRs3Urdu3XIPSkRERESkJKWeA/vss88yefJkEhMTqVWrFikpKezYsYPo6OibGZ+IiIiISCGlLmADAwOJi4tj7dq1pKWl0bBhQ/r164e3t/fNjE9EREREpJBSF7B5eXlUr16dXr16Wbfl5+eTl5dX7DdAye1JC7JERETE1pV6DuzEiRM5ePBgoW0HDx4s8YsCRERERERuhlIXsEePHqVRo0aFtjVs2JAjR46Ue1AiIiIiIiUpdQHr4uLC+fPnC207f/58oa9kFRERERG52Uo9B7Zt27bMmDGDF198kZo1a3Lq1Cn+85//0L695lTakuSo/hUdgk1LrugAKgnl8cYph+VDebxxtzKHtWNn3cLe5HZW6gL2qaee4pNPPuH1118nLy8PR0dH7r33Xp5++umbGZ+IiIiISCGlLmAdHR15+OGHcXd3Jy0tDW9vbzp27Iijo+PNjE9EREREpJBSz4HdvHkzr732GikpKVSvXp2UlBSio6PZvHnzzYxPRERERKSQUo/ALliwgKioKIKCgqzbkpKSmD17NiEhITclOBERERGRq5V6BDY9PZ2mTZsW2hYYGMiZM2fKPSgRERERkZKUegTW39+fZcuWFfomrm+++QZ/f/+bEFbltGTJEtauXYvZbMZkMjFgwIAiz9a9Ij4+ntatW9OuXbtbHKWIiIjI7a3UBWz//v2ZMmUK3333HV5eXpw5cwZHR0dGjhx5M+OrNH7//Xd+/fVXpkyZgoODAxkZGeTn51d0WCIiIiI2p9QFbJ06dZg2bRr79u0jPT0dT09PGjZsiL19qZu4o509exY3NzccHBwAcHd3B2Dx4sX8+uuv5Obm0rhxYwYMGIDJZCp07sGDB/nPf/5DdnY27u7uDBw4EA8PD7799luWL1+OnZ0ddevWZciQIbf6skRERERuuTJVn3Z2dgQGBt6sWCq1li1bsnjxYv75z39y9913ExYWRrNmzXjwwQd54oknAHj33Xf59ddfCy2Ky8/PZ/bs2YwYMQJ3d3cSExNZsGABAwcO5Ouvv+a9997DwcGBixcvVtSliYiIiNxSGj69RZydnZkyZQq7d+8mKSmJadOm8eyzz+Ls7MzSpUvJyckhMzOTevXqFSpgT548ybFjx5gwYQIAFosFDw8PAPz8/HjnnXdo06YNoaGhxfabkJBAQkICADExMUQHRdzkKxUp3twX/vetfd7e3qSlpVVgNLZPOSwfyuONUw6lIqiAvYXMZjPNmzenefPm+Pn5sXz5co4ePcrkyZPx9vZm0aJF5ObmFjmvbt26TJo0qcj26Ohodu3axa+//sqXX37J22+/jZ2dXaFjwsPDCQ8Pv2nXJCIiInKrlfoxWnJjTp48SXLy/74x+vDhw/j6+gKX58NmZ2fzyy+/FDnP19eXjIwMfv/9d+DylIJjx45hsVhIS0sjKCiIZ599lqysLLKzs2/NxYiIiIhUII3A3iLZ2dnMnj2bixcvYmdnR61atRgwYACurq4MGzaM6tWrExAQUOQ8e3t7hg0bxpw5c8jKyqKgoIAePXpQu3Zt3n33XbKysgB46KGHcHV1vdWXJSIiInLLmQzDMCo6CLl1uk/+oqJDkDuU5sCWL+WwfCiPN045LB/KY1FXflNdHE0hEBERERGbogJWRERERGyKClgRERERsSkqYEVERETEpugpBHeYPy6kkbLTJHsREZGKpxFYEREREbEpKmBFRERExKaogBURERERm6ICVkRERERsihZx3WGSo/pXdAg2LbmiA6ggtWNnVXQIIiIiVhqBFRERERGbogJWRERERGyKClgRERERsSkqYEVERETEpqiAFRERERGbcls/haBPnz74+flZX0dFReHj41Nu7W/evJnjx4/Tq1evcmlv3rx5/Pbbb9xzzz3UrFkTJycnunTpUuyxixYtwtnZmUceeaTIvtGjRzNx4sRyiUlERESksrmtC1hHR0diY2PLdI5hGBiGgdl8/cHlkJAQQkJC/mx4RSQkJDBnzpxS9X0tKl5FRERESnZbF7BXy87O5q233uLixYvk5+fz1FNP0aZNG1JTU5k0aRKNGjXi4MGD9O/fnw8//JBGjRrx+++/ExAQQNeuXfn88885f/48//jHP2jYsCGrVq3iwIEDREREEB8fT5UqVTh48CDnzp3jueeeo127dlgsFmbPns3OnTvx8vLC3t6ee++9l3bt2hWKbcqUKWRnZzNy5Egee+wxjh8/bh1h/fbbb1m+fDl2dnbUrVuXIUOGAHD8+HHGjRtHWloaPXr0oEePHgD07duXefPmkZSUxOeff46bmxvHjh2jQYMGDB48GJPJxJYtW/jkk09wcnKiSZMmpKam8tprr93qt0RERETklrutC9jc3FyioqIA8PHx4dVXX2X48OG4uLiQkZHBqFGjrCOoKSkpREZG0rhxY1JTU0lJSeHVV1+lbt26REdHs3btWt544w02b97MkiVLGDFiRJH+zp07xxtvvMHJkyeZMmUK7dq1Y+PGjZw+fZqpU6eSkZHB0KFDuffee4ucO3LkSPr27WsdMV60aJF139dff817772Hg4MDFy9etG4/efIkY8eO5dKlSwwZMoTu3btjb1/4LTl06BBTp07Fw8ODMWPGsHfvXho0aMBHH33E+PHj8fHxYfr06SXmMCEhgYSEBABiYmKIDoooZfavbe4L7culHVvj7e1NWlpaRYchIiJyR7utC9irpxDk5+ezYMECdu/ejclkIj09nfPnzwOXC4vGjRtbj/Xx8bHOn61Xrx533303JpMJPz8/Tp8+XWx/bdq0wWw2U7duXWu7e/bsoV27dpjNZqpXr07z5s3LfB1+fn688847tGnThtDQUOv2Vq1a4eDggIODA9WqVeP8+fN4eXkVOrdhw4bWbf7+/qSmpuLs7IyPj491PnDHjh2tRerVwsPDCQ8PL3PMIiIiIrer27qAvdratWvJyMggJiYGe3t7IiMjyc3NBcDZ2bnQsQ4ODtafTSaT9bXJZMJisRTb/h/PMQzjmrHs27ePDz/8ELi82Oxac2mjo6PZtWsXv/76K19++SVvv/02QKHRVrPZTEFBwTVjMpvNJcYuIiIicqewqQI2KyuLatWqYW9vz86dO0scSS1PTZo0YfXq1XTp0oWMjAySkpLo2LEjjRo1KtUCM4vFQlpaGkFBQQQGBpKYmEh2dvYNxeTr60tqaiqpqan4+PiQmJh4Q+2JiIiI2BKbKmA7duzIlClTGDZsGAEBAdSpU+em99m2bVt27NjBq6++ipeXFw0aNMDFxaXU51ssFt59912ysrIAeOihh3B1db2hmBwdHYmIiODNN9/EycmJgICAG2pPRERExJaYjOv9rlzIzs7G2dmZCxcu8PrrrzNhwgSqV69+W8RkGAYff/wxtWrVomfPntc9r/vkL8qlfy3ikhuhPN445bB8KI83TjksH8pjUb6+viXus6kR2IoSExNjfXTXX//61wovXuHy0wVWr15Nfn4+d911F/fff39FhyQiIiJyS6iALYVx48ZVdAhF9OzZs1QjriIiIiKVzY19ZZSIiIiIyC2mEdg7zJ06d1VEREQqD43AioiIiIhNUQErIiIiIjZFBayIiIiI2BQVsCIiIiJiU7SI6w6THNW/okOwack3uf3asbNucg8iIiK2TyOwIiIiImJTVMCKiIiIiE1RASsiIiIiNkUFrIiIiIjYFBWwIiIiImJTKt1TCC5cuMAbb7wBwLlz5zCbzbi7uwMwefJk7O2vfclJSUksW7aM11577abHWl6WLFnC448/XtFhiIiIiNwSla6AdXNzIzY2FoBFixbh7OzMI488UsFR3RyGYWAYBl9++aUKWBEREbljVLoCtjgHDx7kP//5D9nZ2bi7uzNw4EA8PDxISUnho48+IiMjA7PZzNChQwHIzs4mLi6OY8eO0aBBAwYPHozJZCrU5tmzZ5k+fTpZWVlYLBb69+9P06ZN6du3L/PmzQNgw4YN/Prrr0RGRhIfH4+DgwMHDx7k0qVLPP/887Ru3ZpVq1axceNGsrKySE9Pp1OnTjz55JMAfPPNN/z0008A3HfffTz88MOkpqYyadIkGjVqxMGDB2nYsCG5ublERUVRr149/vGPf9zCzIqIiIjcendEATt79mxGjBiBu7s7iYmJLFiwgIEDB/LOO+/Qq1cvQkNDyc3NxTAMzpw5w6FDh5g6dSoeHh6MGTOGvXv3EhgYWKjNtWvX0rJlSx5//HEsFgs5OTnXjeP06dO8+eabnDp1ivHjx3P33XcDsH//fuLi4nByciI6OppWrVphMpn46aefmDRpEgCvv/46zZo1w9XVlZSUFCIjI2ncuDEA69evt446Xy0hIYGEhAQAYmJiiA6K+NN5rChzX2hf0SFYeXt7k5aWVtFhiIiI3NEqfQGbl5fHsWPHmDBhAgAWiwUPDw8uXbpEeno6oaGhADg6OlrPadiwIV5eXgD4+/uTmppapIANCAjg/fffJz8/n9DQUPz9/a8bS/v27TGbzdSuXZuaNWty8uRJAFq0aIGbmxsAoaGh7NmzB5PJRGhoKM7Oztbtu3fvJiQkBG9vb2vxej3h4eGEh4eX6lgRERERW1DpC1jDMKhbt651JPOKS5culXiOg4OD9Wez2YzFYmHfvn18+OGHAPTp04eQkBDGjx/Pli1biI+Pp2fPnnTp0qXQVIPc3NxC7V49DaEk1zvuSlErIiIicieq9I/RcnBwICMjg99//x2A/Px8jh07RpUqVfDy8mLjxo3A5ZHaa00DaNSoEbGxscTGxhISEsLp06epXr064eHhdOvWjUOHDgFQrVo1jh8/jsVisbZ9xYYNG7BYLKSkpHDq1Cl8fX0B2LFjB5mZmeTm5rJp0yaaNGlCYGAgmzZtIicnh+zsbDZt2kTTpk2Ljc3e3p78/PwbzpWIiIiILaj0I7Amk4lhw4YxZ84csrKyKCgooEePHtSrV49Bgwbx4YcfsmjRIuzs7Hj11VdL3e6Vx23Z2dnh7OzMoEGDAHj22WeZMmUK7u7uNGjQgOzsbOs5Xl5evP7661y6dImXX37ZOm0hICCAuLg4zpw5Q6dOnQgICACga9euvP7668DlRVx33XUXqampRWLp1q0bUVFR3HXXXVrEJSIiIpWeyTAMo6KDuBPEx8fTunVr2rVrV2j7qlWrOHDgABERt2ZxVffJX9ySfsqTFnFVPsrjjVMOy4fyeOOUw/KhPBZ15TfVxan0UwhEREREpHKp9FMIbheRkZHFbu/atStdu3a9tcGIiIiI2DCNwIqIiIiITVEBKyIiIiI2RVMI7jC304IoERERkT9DI7AiIiIiYlNUwIqIiIiITVEBKyIiIiI2RXNg7zDJUf0rOoTbUu3YWRUdgoiIiJSSRmBFRERExKaogBURERERm6ICVkRERERsigpYEREREbEpKmBFRERExKbYTAHbp08foqKiGDZsGFOnTiUnJ6fU5x4+fJgtW7b86b7/+9//Fupv8uTJXLx48U+3JyIiIiJ/ns0UsI6OjsTGxhIXF4e9vT3Lly8vtL+goKDEcw8fPsxvv/32p/v+9ttvCxWw0dHRuLq6/un2bpRhGFgslgrrX0RERKQi2eRzYAMDAzl69ChJSUksXLgQV1dXTp48SWxsLLNmzeLAgQPY2dnx/PPPExgYyMKFC8nNzWXPnj089thjtGrVitmzZ3Ps2DEKCgp48sknadOmDRaLhfnz57Nt2zZMJhPdunXDMAzS09MZP3487u7ujB07lsjISCZPnsyyZcvw8vLiwQcfBGDRokU4OzvzyCOPsHTpUtavX09eXh6hoaH07t27yHXs2rWLOXPmAGAymRg/fjxVqlQp9tzU1FQmTZpEo0aNOHjwIO3btyc7O5u+ffsCsGrVKg4cOEBERMSteyNEREREKoDNFbAFBQVs3bqV4OBgAA4dOkRcXBw+Pj4sW7YMgLi4OE6cOMHEiROZMWMGffr0KVTc/d///R9BQUEMHDiQixcv8vrrr3P33Xfz888/c/r0ad566y3s7OzIzMykatWq/Pe//2Xs2LG4u7sXiiUsLIy5c+daC9j169czatQotm3bRnJyMm+++SaGYfDWW2+xa9cumjVrVuj8pUuXEhERQWBgINnZ2Tg4OJR4rre3NykpKURGRtK4cWMyMjIYNWqUtYBNTEzk8ccfv5mpFxEREbkt2EwBm5ubS1RUFABNmzblvvvuY+/evTRs2BAfHx8A9uzZw0MPPQRAnTp1qFGjBsnJyUXa2r59O7/++qu14M3NzSUtLY3t27fTvXt37OzsAKhateo1Y7rrrrvIyMggPT2djIwMqlatire3N99++y3bt29nxIgRAGRnZ5OSklKkgA0MDOSTTz6hY8eOtG3bFi8vL7Zt21bsud7e3nh7e9O4cWMA3N3dqVmzJr///ju1a9fmxIkTNGnSpEiMCQkJJCQkABATE0N0UMWP0M59oX1FhyAiIiI2zGYK2CtzYK/m5ORU5rYMw2DYsGH4+vrecFzt2rVjw4YNnDt3jvbt/1eY9erVi/vvv7/Qsd9//z0rVqwALs+j7dWrF61atWLLli2MGTOGUaNGlXhuamoqzs7OhbaFhYWxfv166tSpQ2hoKCaTqUh84eHhhIeH3/B1ioiIiNwubGYRV2k0bdqUNWvWAHDy5EnS0tLw9fXF2dmZS5cuWY9r2bIl3333HYZhAJenIQC0aNGC5cuXWxeEZWZmAuDs7Ex2dnaxfYaFhZGYmMgvv/xiLWBbtmzJTz/9ZD0nPT2d8+fP8+CDDxIbG0tsbCyenp6kpKTg5+dHr169CAgI4MSJEyWeW5zQ0FA2b97MunXr6NChww3lTkRERMRW2MwIbGl0796dWbNmMWzYMOzs7Bg4cCAODg4EBQXx9ddfExUVxWOPPcYTTzzB3LlzGT58OIZh4OPjw2uvvUa3bt1ITk5m+PDh2Nvb061bNx588EHCw8OZNGkSnp6ejB07tlCf9erV49KlS3h6euLh4QFcLmBPnDhhHVF1dnZm8ODBVKtWrdC53377LUlJSZhMJurWrcs999yDg4NDseeazUX/r1G1alXq1KnD8ePHadiw4c1IqYiIiMhtx2RcGYaUO0L3yV9UdAg2PQfW29ubtLS0ig7D5imPN045LB/K441TDsuH8ljUtaZ6VqopBCIiIiJS+amAFRERERGbogJWRERERGyKClgRERERsSmV6ikEcn22vIBKREREBDQCKyIiIiI2RgWsiIiIiNgUFbAiIiIiYlNUwIqIiIiITVEBKyIiIiI2RQWsiIiIiNgUFbAiIiIiYlNUwIqIiIiITVEBKyIiIiI2pVIUsBs3bqR3796cOHHilvQXGRlJRkZGke2rVq0iIiKCqKgooqKieO+9925JPCIiIiJ3kkrxVbLr1q0jMDCQdevW0bt370L7CgoKsLOzu2WxhIWFERERUaZzDMPAMAzM5krx/wkRERGRm8rmC9js7Gz27NnD2LFjmTJlCr179yYpKYmFCxfi6urKyZMnGTBgAIsWLcLV1ZWjR4/Svn17/Pz8+Pbbb8nNzSUqKopatWqRkZHBhx9+yJkzZwB44YUXCAwM5MKFC8yYMYP09HQaN26MYRhlivGbb77hp59+AuC+++7j4YcfJjU1lUmTJtGoUSMOHjxIdHQ069atY82aNZjNZoKDg3n22WdJSUnh448/JiMjAycnJ/72t79Rp04d1q9fz+LFizGbzbi4uDB+/Phyz62IiIjI7cjmC9hNmzYRHByMr68vbm5uHDx4EIBDhw4RFxeHj48PSUlJHDlyhGnTplG1alUGDRpEt27dmDx5Mt9++y3ff/89/fr1Y86cOfTs2ZPAwEDS0tKYNGkS06ZN4/PPPycwMJAnnniCLVu2sHLlyhLjSUxMZM+ePQD06NGD+vXr89NPPzFp0iQAXn/9dZo1a4arqyspKSlERkbSuHFjfvvtNzZv3sybb76Jk5MTmZmZAHz44Ye8/PLL1K5dm3379jFr1izGjh3L4sWLGTVqFJ6enly8ePEmZ1lERETk9mHzBey6devo0aMHcPnX92vXrqV169Y0bNgQHx8f63EBAQF4eHgAUKtWLVq0aAGAn58fO3fuBGDHjh0cP37cek5WVhbZ2dns3r2b4cOHA9CqVStcXV1LjOfqKQTffvstoaGhODs7AxAaGsru3bsJCQnB29ubxo0bW/vu2rUrTk5OAFStWpXs7Gz27t3L1KlTre3l5+cD0KRJE+Lj42nfvj1t27YtMZ6EhAQSEhIAiImJKTmRIiIiIjbCpgvYzMxMdu7cydGjRzGZTFgsFuBykXmlELzCwcHB+rPJZLK+/uN5hmEwadIkHB0dS9X/999/z4oVKwCIjo4uc/xXitqSWCwWXF1diY2NLbJvwIAB7Nu3jy1btvDaa68RExODm5tbkePCw8MJDw8vc2wiIiIityubXjW0YcMGOnfuzMyZM4mPj+f999/Hx8fH+iv8smrRogXff/+99fXhw4cBaNq0KWvXrgXgt99+s/7K/sEHHyQ2NpbY2Fg8PT2LbTMwMJBNmzaRk5NDdnY2mzZtomnTpsX2vWrVKnJycoDLxbmLiws+Pj6sX78euFxgX4kpJSWFRo0a0adPH9zd3a3zdkVEREQqO5segV23bh2PPvpooW1t27Zl+fLl1KxZs8ztvfjii3z88ccMHz6cgoICmjZtyoABA3jyySeZMWMGr776Ko0bN8bb27vUbTZo0ICuXbvy+uuvA5cXcd11112kpqYWOi44OJjDhw/z2muvYW9vzz333MMzzzzDP/7xDz766COWLFlCfn4+HTp0wN/fn/nz55OcnAxAUFAQ9evXL/P1ioiIiNgik1HWJfVi006ePFnRIdg0b29v0tLSKjoMm6c83jjlsHwojzdOOSwfymNRvr6+Je6z6SkEIiIiInLnUQErIiIiIjZFBayIiIiI2BQVsCIiIiJiU1TAioiIiIhNUQErIiIiIjZFBayIiIiI2BQVsCIiIiJiU1TAioiIiIhNsemvkpWyS47qf9Parh0766a1LSIiInKFRmBFRERExKaogBURERERm6ICVkRERERsigpYEREREbEpKmBFRERExKbc9gVsnz59iIqKYtiwYUydOpWcnJxijxs9enSZ2u3bt295hCciIiIit9htX8A6OjoSGxtLXFwc9vb2LF++vND+goICACZOnFgR4VUYi8VS0SGIiIiIVAibeg5sYGAgR48eJSkpiYULF+Lq6srJkyeZMWMGffv2Zd68eUyfPp3OnTvTqlUrAOLj42ndujXt2rUr0t6CBQvYsmULjo6OREVFUb16dVJTU3n//fe5cOEC7u7uDBw4EG9v7yLtXOnv7NmzTJ8+naysLCwWC/3796dp06Zs27aNRYsWkZ+fT82aNRk4cCDOzs6F+i/ruZGRkbRv354dO3bQvn17fvnlFyZPngxAamoqU6ZMIS4u7ia/CyIiIiIVy2YK2IKCArZu3UpwcDAAhw4dIi4uDh8fn0LHhYWFkZiYSKtWrcjPz2fnzp28/PLLRdrLycmhUaNGPP3008yfP58VK1bw17/+ldmzZ9OlSxe6du3KypUrmT17NiNGjCgxrrVr19KyZUsef/xxLBYLOTk5ZGRksGTJEsaMGYOzszNfffUV33zzDU888cQNn+vm5saUKVMAWLduHampqfj4+JCYmEhYWFiR+BISEkhISAAgJiZGXzYgIiIiNu+2L2Bzc3OJiooCoGnTptx3333s3buXhg0bFileAYKDg5kzZw55eXls3bqVpk2b4ujoWOQ4e3t7WrduDUCDBg3Yvn07APv27WP48OEAdO7cmU8//fSa8QUEBPD++++Tn59PaGgo/v7+7Nq1i+PHjzNmzBgA8vPzady4cbmc+8citX379iQmJtKrVy/Wr1/PkCFDivQRHh5OeHj4Na9BRERExJbc9gXslTmwV3Nycirx+ObNm7Nt2zYSExPp0KEDFouFkSNHAhASEkKfPn2ws7PDZDIBYDabrXNpS2JnZ2edd2qxWMjPzwegWbNmjB8/ni1bthAfH0/Pnj1xdXXl7rvvLlJQ7tu3jw8//BC4vDgtJCSk1OcWd91hYWFMmzaN0NBQAGrXrn3NaxARERGpDG77AvbPCAsLY8WKFRw8eJDIyEjMZnOxRXBxGjduTGJiIp07d2bt2rUEBgYCUKNGDQ4ePEhYWBibN2+2FrynT5/Gy8uL8PBw8vPzOXToEI8//jgff/wxKSkp1KpVi+zsbNLT02nUqFGhOMpyrq+vb5FYa9Wqhdls5osvvih2+oCIiIhIZVQpC9gWLVrw7rvv0qZNG+zty3aJL730EjNnzmTp0qXWRVwA3bp1IzY2lqioKFq2bGkdCU1KSmLZsmXY2dnh7OzMoEGDcHd3JzIykhkzZpCXlwfAU089VaQIvZFzr2jfvj3z58/nvffeK9N1ioiIiNgqk2EYRkUHIbfOyZMnKzoEm+bt7U1aWlpFh2HzlMcbpxyWD+XxximH5UN5LKqkwTuwgefAioiIiIj8kQpYEREREbEpKmBFRERExKaogBURERERm6ICVkRERERsigpYEREREbEpeoyWiIiIiNgUjcDeQV577bWKDsHmKYflQ3m8ccph+VAeb5xyWD6Ux7JRASsiIiIiNkUFrIiIiIjYFBWwd5Dw8PCKDsHmKYflQ3m8ccph+VAeb5xyWD6Ux7LRIi4RERERsSkagRURERERm2Jf0QHIzbd161bmzJmDxWKhW7du9OrVq6JDuq1FRkbi7OyM2WzGzs6OmJgYMjMzmTZtGqdPn6ZGjRoMHTqUqlWrYhgGc+bM4bfffsPJyYmBAwfSoEGDir6EW27mzJls2bKFatWqERcXB/CncrZq1SqWLFkCwOOPP07Xrl0r6pIqRHF5XLRoEStWrMDd3R2Ap59+mlatWgHw5ZdfsnLlSsxmMy+++CLBwcHAnf2ZT0tLIz4+nnPnzmEymQgPD6dHjx66H8ugpBzqXiyb3Nxcxo4dS35+PgUFBbRr147evXuTmprK9OnTuXDhAg0aNGDw4MHY29uTl5fHe++9x8GDB3Fzc2PIkCH4+PgAJef3jmZIpVZQUGAMGjTISElJMfLy8ozhw4cbx44dq+iwbmsDBw40zp8/X2jbvHnzjC+//NIwDMP48ssvjXnz5hmGYRi//vqrMWnSJMNisRh79+41oqOjb3W4t4WkpCTjwIEDxquvvmrdVtacXbhwwYiMjDQuXLhQ6Oc7SXF5XLhwofH1118XOfbYsWPG8OHDjdzcXOPUqVPGoEGDjIKCgjv+M5+enm4cOHDAMAzDyMrKMv7xj38Yx44d0/1YBiXlUPdi2VgsFuPSpUuGYRhGXl6eER0dbezdu9eIi4sz1q5daxiGYfz73/82fvjhB8MwDOP77783/v3vfxuGYRhr1641pk6dahhGyfm902kKQSW3f/9+atWqRc2aNbG3tycsLIxNmzZVdFg2Z9OmTXTp0gWALl26WHO4efNmOnfujMlkonHjxly8eJGzZ89WZKgVolmzZlStWrXQtrLmbOvWrbRo0YKqVatStWpVWrRowdatW2/1pVSo4vJYkk2bNhEWFoaDgwM+Pj7UqlWL/fv33/GfeQ8PD+sIapUqVahTpw7p6em6H8ugpByWRPdi8UwmE87OzgAUFBRQUFCAyWQiKSmJdu3aAdC1a9dC9+KVUf527dqxc+dODMMoMb93Ok0hqOTS09Px8vKyvvby8mLfvn0VGJFtmDRpEgD3338/4eHhnD9/Hg8PDwCqV6/O+fPngcv59fb2tp7n5eVFenq69dg7WVlzdvW96unpec1/NO8kP/zwAz///DMNGjTg+eefp2rVqqSnp9OoUSPrMX/Mlz7zl6WmpnLo0CEaNmyo+/FP+mMO9+zZo3uxjCwWCyNHjiQlJYUHHniAmjVr4uLigp2dHVA4V3+85+zs7HBxceHChQvXzO+dTAWsyFUmTJiAp6cn58+fZ+LEifj6+hbabzKZMJlMFRSdbVLO/rzu3bvzxBNPALBw4UI++eQTBg4cWMFR3f6ys7OJi4ujX79+uLi4FNqn+7F0rs6h7sWyM5vNxMbGcvHiRd5++21OnjxZ0SFVGppCUMl5enpy5swZ6+szZ87g6elZgRHd/q7kp1q1arRp04b9+/dTrVo169SAs2fPWhcxeHp6kpaWZj1X+f2fsubs6ns1PT1dueTyaKHZbMZsNtOtWzcOHDgAFP1sX8mXPvOQn59PXFwcnTp1om3btoDux7IqLoe6F/88V1dXmjdvzu+//05WVhYFBQVA4fvqj/kqKCggKysLNze3O/5eLIkK2EouICCA5ORkUlNTyc/PJzExkZCQkIoO67aVnZ3NpUuXrD9v374dPz8/QkJCWL16NQCrV6+mTZs2AISEhPDzzz9jGAa///47Li4umj7w/5U1Z8HBwWzbto3MzEwyMzPZtm2bVtpCoTnVGzdupF69esDlPCYmJpKXl0dqairJyck0bNjwjv/MG4bBBx98QJ06dejZs6d1u+7H0isph7oXyyYjI4OLFy8Cl59IsH37durUqUPz5s3ZsGEDcPlJF1dy0rp1a1atWgXAhg0baN68OSaTqcT83un0RQZ3gC1btvCf//wHi8XCvffey+OPP17RId22Tp06xdtvvw1c/h9wx44defzxx7lw4QLTpk0jLS2tyCN4Pv74Y7Zt24ajoyMDBw4kICCggq/i1ps+fTq7du3iwoULVKtWjd69e9OmTZsy52zlypV8+eWXwOXHFt17770VeVm3XHF5TEpK4vDhw5hMJmrUqMGAAQOs/0lasmQJP/30E2azmX79+nHPPfcAd/Znfs+ePfzrX//Cz8/POk3g6aefplGjRrofS6mkHK5bt073YhkcOXKE+Ph4LBYLhmHQvn17nnjiCU6dOsX06dPJzMzkrrvuYvDgwTg4OJCbm8t7773HoUOHqFq1KkOGDKFmzZpAyfm9k6mAFRERERGboikEIiIiImJTVMCKiIiIiE1RASsiIiIiNkUFrIiIiIjYFBWwIiIiImJTVMCKiIiIiE1RASsiIrdcZGQk27dvr+gwRMRGqYAVEREREZuiLzIQERHS0tKYO3cuu3fvxjAMOnTowIsvvsiXX37JihUryM3NJTg4mJdeegkXFxeSkpJ49913+eCDD6xtREZG8re//Y0WLVqwaNEijh8/jqOjIxs3bsTb25vIyEgCAgJ49913Wbt2Lfb29pjNZp544gkeffTRCrx6EbE1GoEVEbnDWSwWpkyZgre3N/Hx8XzwwQd06NCBVatWsWrVKsaOHct7771HdnY2H3/8canb/fXXXwkLC2Pu3LmEhIQwe/ZsAAYPHoy3tzcjR45k3rx5Kl5FpMxUwIqI3OH2799Peno6ffv2xdnZGUdHRwIDA1m7di09e/akZs2aODs788wzz5CYmEhBQUGp2g0MDKRVq1aYzWY6d+7M4cOHb+6FiMgdQwWsiMgdLi0tjRo1amBnZ1do+9mzZ6lRo4b1tbe3NwUFBZw/f75U7VarVs36s6OjI3l5eaUufkVErkUFrIjIHc7b25u0tLQixaWHhwenT5+2vk5LS8POzo5q1arh5ORETk6OdZ/FYiEjI+OWxSwidzYVsCIid7iGDRvi4eHBp59+SnZ2Nrm5uezZs4cOHTrw3//+l9TUVLKzs1mwYAHt27fHzs4OX19f8vLy2LJlC/n5+XzxxRfk5eWVus/q1auTmpp6E69KRCoz+4oOQEREKpbZbGbkyJHMnj2bgQMHYjKZ6NChA/369ePs2bOMHTuW3NxcWrZsyUsvvQSAi4sL/fv354MPPsBisfDII4/g5eVV6j579erF7NmzmT9/Po8//jiPPPLIzbo8EamE9BgtEREREbEpmkIgIiIiIjZFBayIiIiI2BQVsCIiIiJiU1TAioiIiIhNUQErIiIiIjZFBayIiIiI2BQVsCIiIiJiU1TAioiIiIhNUQErIiIiIjbl/wEiv6SJKBlVvQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('ggplot')\n", + "plt.figure(figsize = (10,6))\n", + "plt.title(\"Income of people according to their occupation\", fontsize = 16)\n", + "sns.countplot(y = ds['occupation'], hue = ds['income'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.072034, + "end_time": "2021-05-22T12:30:15.726655", + "exception": false, + "start_time": "2021-05-22T12:30:15.654621", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Majority of people whose income is greater than 50K are either executive managers or they belong to any professional speciality. Now, encoding the occupation by frequency of the values in the column." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:15.879859Z", + "iopub.status.busy": "2021-05-22T12:30:15.879208Z", + "iopub.status.idle": "2021-05-22T12:30:16.061079Z", + "shell.execute_reply": "2021-05-22T12:30:16.061999Z", + "shell.execute_reply.started": "2021-05-22T11:18:10.082984Z" + }, + "papermill": { + "duration": 0.262366, + "end_time": "2021-05-22T12:30:16.062268", + "exception": false, + "start_time": "2021-05-22T12:30:15.799902", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "occupation\n", + "Adm-clerical 0.122729\n", + "Armed-Forces 0.000293\n", + "Craft-repair 0.133440\n", + "Exec-managerial 0.132365\n", + "Farming-fishing 0.032359\n", + "Handlers-cleaners 0.044599\n", + "Machine-op-inspct 0.065174\n", + "Other-service 0.107266\n", + "Priv-house-serv 0.004851\n", + "Prof-specialty 0.134774\n", + "Protective-serv 0.021128\n", + "Sales 0.118823\n", + "Tech-support 0.030210\n", + "Transport-moving 0.051989\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "1 0.132365\n", + "3 0.065174\n", + "4 0.134774\n", + "Name: occupation_enc, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "occupation_enc = (ds.groupby('occupation').size()) / len(ds)\n", + "print(occupation_enc)\n", + "\n", + "ds['occupation_enc'] = ds['occupation'].apply(lambda x : occupation_enc[x])\n", + "ds['occupation_enc'].head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:16.215478Z", + "iopub.status.busy": "2021-05-22T12:30:16.214798Z", + "iopub.status.idle": "2021-05-22T12:30:16.221828Z", + "shell.execute_reply": "2021-05-22T12:30:16.221166Z", + "shell.execute_reply.started": "2021-05-22T11:18:17.564935Z" + }, + "papermill": { + "duration": 0.085262, + "end_time": "2021-05-22T12:30:16.222113", + "exception": false, + "start_time": "2021-05-22T12:30:16.136851", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds.drop(['occupation'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.073071, + "end_time": "2021-05-22T12:30:16.370188", + "exception": false, + "start_time": "2021-05-22T12:30:16.297117", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**'Native_country':** We are checking for the salary on people in USA and outside USA, so , **we will convert all the values where country is not USA to 'non-usa'.** This way, we can encode the values by one-hot encoding without increasing the curse of dimensionality." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:16.540833Z", + "iopub.status.busy": "2021-05-22T12:30:16.528646Z", + "iopub.status.idle": "2021-05-22T12:30:16.547837Z", + "shell.execute_reply": "2021-05-22T12:30:16.547275Z", + "shell.execute_reply.started": "2021-05-22T11:18:32.023074Z" + }, + "papermill": { + "duration": 0.104832, + "end_time": "2021-05-22T12:30:16.547987", + "exception": false, + "start_time": "2021-05-22T12:30:16.443155", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "usa 27504\n", + "non_usa 3214\n", + "Name: native.country, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds['native.country'].loc[ds['native.country'] == 'United-States'] = 'usa'\n", + "ds['native.country'].loc[ds['native.country'] != 'usa'] = 'non_usa'\n", + "ds['native.country'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:16.700829Z", + "iopub.status.busy": "2021-05-22T12:30:16.699868Z", + "iopub.status.idle": "2021-05-22T12:30:16.703186Z", + "shell.execute_reply": "2021-05-22T12:30:16.702687Z", + "shell.execute_reply.started": "2021-05-22T11:18:47.333522Z" + }, + "papermill": { + "duration": 0.082085, + "end_time": "2021-05-22T12:30:16.703328", + "exception": false, + "start_time": "2021-05-22T12:30:16.621243", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('default')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:16.856265Z", + "iopub.status.busy": "2021-05-22T12:30:16.855165Z", + "iopub.status.idle": "2021-05-22T12:30:16.858346Z", + "shell.execute_reply": "2021-05-22T12:30:16.857770Z", + "shell.execute_reply.started": "2021-05-22T11:18:57.637403Z" + }, + "papermill": { + "duration": 0.081734, + "end_time": "2021-05-22T12:30:16.858501", + "exception": false, + "start_time": "2021-05-22T12:30:16.776767", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('seaborn-pastel')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:17.051126Z", + "iopub.status.busy": "2021-05-22T12:30:17.050103Z", + "iopub.status.idle": "2021-05-22T12:30:17.217934Z", + "shell.execute_reply": "2021-05-22T12:30:17.217358Z", + "shell.execute_reply.started": "2021-05-22T11:19:20.052456Z" + }, + "papermill": { + "duration": 0.285384, + "end_time": "2021-05-22T12:30:17.218090", + "exception": false, + "start_time": "2021-05-22T12:30:16.932706", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (8,3))\n", + "plt.title(\"Income of people according to their native country\", fontsize = 16)\n", + "sns.countplot(y = ds['native.country'], hue = ds['income'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.073712, + "end_time": "2021-05-22T12:30:17.366425", + "exception": false, + "start_time": "2021-05-22T12:30:17.292713", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Majority of people with higher income belong to the USA**. We also have more number of people from USA then any other country combined in this dataset. Encoding this feature using one hot encoding." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:17.525945Z", + "iopub.status.busy": "2021-05-22T12:30:17.523481Z", + "iopub.status.idle": "2021-05-22T12:30:17.532455Z", + "shell.execute_reply": "2021-05-22T12:30:17.532972Z", + "shell.execute_reply.started": "2021-05-22T11:20:34.968882Z" + }, + "papermill": { + "duration": 0.092714, + "end_time": "2021-05-22T12:30:17.533176", + "exception": false, + "start_time": "2021-05-22T12:30:17.440462", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds['country_enc'] = ds['native.country'].map({'usa' : 1, 'non_usa' : 0})\n", + "ds.drop(['native.country'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.076047, + "end_time": "2021-05-22T12:30:17.683866", + "exception": false, + "start_time": "2021-05-22T12:30:17.607819", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**'Sex':** Similarly, encoding the sex using one hot encoding." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:17.839468Z", + "iopub.status.busy": "2021-05-22T12:30:17.838460Z", + "iopub.status.idle": "2021-05-22T12:30:18.035979Z", + "shell.execute_reply": "2021-05-22T12:30:18.036582Z", + "shell.execute_reply.started": "2021-05-22T11:21:11.594136Z" + }, + "papermill": { + "duration": 0.277147, + "end_time": "2021-05-22T12:30:18.036773", + "exception": false, + "start_time": "2021-05-22T12:30:17.759626", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Income of people by their sex\", fontsize = 16)\n", + "sns.countplot(x = ds['sex'], hue = ds['income'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.075084, + "end_time": "2021-05-22T12:30:18.188189", + "exception": false, + "start_time": "2021-05-22T12:30:18.113105", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We can see that male have more salary than female. Also in the dataset, the number of men are more than women. Encoding this feature with one hot encoding." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:18.347438Z", + "iopub.status.busy": "2021-05-22T12:30:18.346462Z", + "iopub.status.idle": "2021-05-22T12:30:18.360862Z", + "shell.execute_reply": "2021-05-22T12:30:18.361439Z", + "shell.execute_reply.started": "2021-05-22T11:22:31.975764Z" + }, + "papermill": { + "duration": 0.094795, + "end_time": "2021-05-22T12:30:18.361621", + "exception": false, + "start_time": "2021-05-22T12:30:18.266826", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds['sex_enc'] = ds['sex'].map({'Male' : 1, 'Female' : 0})\n", + "ds.drop(['sex'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.074775, + "end_time": "2021-05-22T12:30:18.511496", + "exception": false, + "start_time": "2021-05-22T12:30:18.436721", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**'Marital_status':** Looking at the iincome of people according to their marital status." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:18.666227Z", + "iopub.status.busy": "2021-05-22T12:30:18.665253Z", + "iopub.status.idle": "2021-05-22T12:30:18.670553Z", + "shell.execute_reply": "2021-05-22T12:30:18.670977Z", + "shell.execute_reply.started": "2021-05-22T11:23:38.581457Z" + }, + "papermill": { + "duration": 0.084086, + "end_time": "2021-05-22T12:30:18.671178", + "exception": false, + "start_time": "2021-05-22T12:30:18.587092", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('default')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:18.826649Z", + "iopub.status.busy": "2021-05-22T12:30:18.825627Z", + "iopub.status.idle": "2021-05-22T12:30:18.829705Z", + "shell.execute_reply": "2021-05-22T12:30:18.830230Z", + "shell.execute_reply.started": "2021-05-22T11:23:46.109277Z" + }, + "papermill": { + "duration": 0.083836, + "end_time": "2021-05-22T12:30:18.830419", + "exception": false, + "start_time": "2021-05-22T12:30:18.746583", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('seaborn-talk')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:18.985529Z", + "iopub.status.busy": "2021-05-22T12:30:18.984403Z", + "iopub.status.idle": "2021-05-22T12:30:19.272397Z", + "shell.execute_reply": "2021-05-22T12:30:19.271868Z", + "shell.execute_reply.started": "2021-05-22T11:24:10.729192Z" + }, + "papermill": { + "duration": 0.366989, + "end_time": "2021-05-22T12:30:19.272552", + "exception": false, + "start_time": "2021-05-22T12:30:18.905563", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Income of people by Marital Status\", fontsize = 16)\n", + "sns.countplot(y = ds['marital.status'], hue = ds['income'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.076191, + "end_time": "2021-05-22T12:30:19.425779", + "exception": false, + "start_time": "2021-05-22T12:30:19.349588", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Married people have a higher income as compared to others.** Encoding the feature" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:19.585729Z", + "iopub.status.busy": "2021-05-22T12:30:19.584948Z", + "iopub.status.idle": "2021-05-22T12:30:19.773916Z", + "shell.execute_reply": "2021-05-22T12:30:19.773318Z", + "shell.execute_reply.started": "2021-05-22T11:25:17.105957Z" + }, + "papermill": { + "duration": 0.271368, + "end_time": "2021-05-22T12:30:19.774079", + "exception": false, + "start_time": "2021-05-22T12:30:19.502711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "marital.status\n", + "Divorced 0.138616\n", + "Married-AF-spouse 0.000684\n", + "Married-civ-spouse 0.466795\n", + "Married-spouse-absent 0.012664\n", + "Never-married 0.322677\n", + "Separated 0.031219\n", + "Widowed 0.027346\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "1 0.027346\n", + "3 0.138616\n", + "4 0.031219\n", + "Name: marital_status_enc, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "marital_status_enc = (ds.groupby('marital.status').size()) / len(ds)\n", + "print(marital_status_enc)\n", + "\n", + "ds['marital_status_enc'] = ds['marital.status'].apply(lambda x : marital_status_enc[x])\n", + "ds['marital_status_enc'].head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:19.935490Z", + "iopub.status.busy": "2021-05-22T12:30:19.934671Z", + "iopub.status.idle": "2021-05-22T12:30:19.941378Z", + "shell.execute_reply": "2021-05-22T12:30:19.940632Z", + "shell.execute_reply.started": "2021-05-22T11:25:47.132929Z" + }, + "papermill": { + "duration": 0.089231, + "end_time": "2021-05-22T12:30:19.941535", + "exception": false, + "start_time": "2021-05-22T12:30:19.852304", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ds.drop(['marital.status'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.078968, + "end_time": "2021-05-22T12:30:20.098225", + "exception": false, + "start_time": "2021-05-22T12:30:20.019257", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Similarly, **for 'Race' and 'Relationship'**" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:20.261116Z", + "iopub.status.busy": "2021-05-22T12:30:20.260120Z", + "iopub.status.idle": "2021-05-22T12:30:20.263389Z", + "shell.execute_reply": "2021-05-22T12:30:20.262864Z", + "shell.execute_reply.started": "2021-05-22T11:26:11.358015Z" + }, + "papermill": { + "duration": 0.086967, + "end_time": "2021-05-22T12:30:20.263537", + "exception": false, + "start_time": "2021-05-22T12:30:20.176570", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('bmh')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:20.443373Z", + "iopub.status.busy": "2021-05-22T12:30:20.442238Z", + "iopub.status.idle": "2021-05-22T12:30:20.905290Z", + "shell.execute_reply": "2021-05-22T12:30:20.905794Z", + "shell.execute_reply.started": "2021-05-22T11:27:07.272687Z" + }, + "papermill": { + "duration": 0.56455, + "end_time": "2021-05-22T12:30:20.905967", + "exception": false, + "start_time": "2021-05-22T12:30:20.341417", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,4))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "sns.countplot(y = ds['race'], hue = ds['income'])\n", + "plt.title(\"Income respective to Race\", fontsize = 12)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sns.countplot(y = ds['relationship'], hue = ds['income'])\n", + "plt.title(\"Income respective to Relationship\", fontsize = 12)\n", + "\n", + "plt.tight_layout(pad = 4)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.080488, + "end_time": "2021-05-22T12:30:21.066513", + "exception": false, + "start_time": "2021-05-22T12:30:20.986025", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**White people have a higher salary as compared to other races**. Similarly, **husband in the family have a higher salary as compared to other relationship in the family.** Encoding both these columns" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:21.241277Z", + "iopub.status.busy": "2021-05-22T12:30:21.240306Z", + "iopub.status.idle": "2021-05-22T12:30:21.630387Z", + "shell.execute_reply": "2021-05-22T12:30:21.628912Z", + "shell.execute_reply.started": "2021-05-22T11:28:23.336326Z" + }, + "papermill": { + "duration": 0.48318, + "end_time": "2021-05-22T12:30:21.630669", + "exception": false, + "start_time": "2021-05-22T12:30:21.147489", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "race\n", + "Amer-Indian-Eskimo 0.009311\n", + "Asian-Pac-Islander 0.031708\n", + "Black 0.094700\n", + "Other 0.008073\n", + "White 0.856208\n", + "dtype: float64 \n", + "\n", + "relationship\n", + "Husband 0.413569\n", + "Not-in-family 0.256039\n", + "Other-relative 0.029885\n", + "Own-child 0.147308\n", + "Unmarried 0.106485\n", + "Wife 0.046715\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "race_enc = (ds.groupby('race').size()) / len(ds)\n", + "print(race_enc,'\\n')\n", + "ds['race_enc'] = ds['race'].apply(lambda x : race_enc[x])\n", + "\n", + "relationship_enc = (ds.groupby('relationship').size()) / len(ds)\n", + "print(relationship_enc)\n", + "ds['relationship_enc'] = ds['relationship'].apply(lambda x : relationship_enc[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:21.800211Z", + "iopub.status.busy": "2021-05-22T12:30:21.799498Z", + "iopub.status.idle": "2021-05-22T12:30:21.829370Z", + "shell.execute_reply": "2021-05-22T12:30:21.828832Z", + "shell.execute_reply.started": "2021-05-22T11:29:15.365794Z" + }, + "papermill": { + "duration": 0.116653, + "end_time": "2021-05-22T12:30:21.829529", + "exception": false, + "start_time": "2021-05-22T12:30:21.712876", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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30718 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " age fnlwgt education.num capital.gain capital.loss hours.per.week \\\n", + "1 82 132870 9 0 4356 18 \n", + "3 54 140359 4 0 3900 40 \n", + "4 41 264663 10 0 3900 40 \n", + "5 34 216864 9 0 3770 45 \n", + "6 38 150601 6 0 3770 40 \n", + "... ... ... ... ... ... ... \n", + "32556 22 310152 10 0 0 40 \n", + "32557 27 257302 12 0 0 38 \n", + "32558 40 154374 9 0 0 40 \n", + "32559 58 151910 9 0 0 40 \n", + "32560 22 201490 9 0 0 20 \n", + "\n", + " workclass_enc occupation_enc country_enc sex_enc \\\n", + "1 0.69703 0.132365 1 0 \n", + "3 0.69703 0.065174 1 0 \n", + "4 0.69703 0.134774 1 0 \n", + "5 0.69703 0.107266 1 0 \n", + "6 0.69703 0.122729 1 1 \n", + "... ... ... ... ... \n", + "32556 0.69703 0.021128 1 1 \n", + "32557 0.69703 0.030210 1 0 \n", + "32558 0.69703 0.065174 1 1 \n", + "32559 0.69703 0.122729 1 0 \n", + "32560 0.69703 0.122729 1 1 \n", + "\n", + " marital_status_enc race_enc relationship_enc income \n", + "1 0.027346 0.856208 0.256039 0 \n", + "3 0.138616 0.856208 0.106485 0 \n", + "4 0.031219 0.856208 0.147308 0 \n", + "5 0.138616 0.856208 0.106485 0 \n", + "6 0.031219 0.856208 0.106485 0 \n", + "... ... ... ... ... \n", + "32556 0.322677 0.856208 0.256039 0 \n", + "32557 0.466795 0.856208 0.046715 0 \n", + "32558 0.466795 0.856208 0.413569 1 \n", + "32559 0.027346 0.856208 0.106485 0 \n", + "32560 0.322677 0.856208 0.147308 0 \n", + "\n", + "[30718 rows x 14 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.drop(['race', 'relationship'], axis = 1, inplace = True)\n", + "new_ds = ds.drop(['income'], axis = 1)\n", + "new_ds['income'] = ds['income']\n", + "new_ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.081008, + "end_time": "2021-05-22T12:30:21.994515", + "exception": false, + "start_time": "2021-05-22T12:30:21.913507", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Outliers:\n", + "We check if any outliers are present in the continous attributes of the dataset. We check it both by visualisations and the zscore for the continous columns." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:22.165508Z", + "iopub.status.busy": "2021-05-22T12:30:22.164804Z", + "iopub.status.idle": "2021-05-22T12:30:22.168031Z", + "shell.execute_reply": "2021-05-22T12:30:22.167489Z", + "shell.execute_reply.started": "2021-05-22T11:30:07.896274Z" + }, + "papermill": { + "duration": 0.091383, + "end_time": "2021-05-22T12:30:22.168185", + "exception": false, + "start_time": "2021-05-22T12:30:22.076802", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('default')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:22.335724Z", + "iopub.status.busy": "2021-05-22T12:30:22.335051Z", + "iopub.status.idle": "2021-05-22T12:30:22.338498Z", + "shell.execute_reply": "2021-05-22T12:30:22.337777Z", + "shell.execute_reply.started": "2021-05-22T11:30:17.131225Z" + }, + "papermill": { + "duration": 0.089434, + "end_time": "2021-05-22T12:30:22.338665", + "exception": false, + "start_time": "2021-05-22T12:30:22.249231", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:22.528776Z", + "iopub.status.busy": "2021-05-22T12:30:22.528095Z", + "iopub.status.idle": "2021-05-22T12:30:23.037634Z", + "shell.execute_reply": "2021-05-22T12:30:23.038156Z", + "shell.execute_reply.started": "2021-05-22T11:31:07.701315Z" + }, + "papermill": { + "duration": 0.618655, + "end_time": "2021-05-22T12:30:23.038333", + "exception": false, + "start_time": "2021-05-22T12:30:22.419678", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BoxPlots of the features:\n" + ] + }, + { + "data": { + "image/png": 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ybSc8PDzfMnm3Ya+Ps/W7Ms9VpVlHQcvmbXNUVJTq1Knjcp0AoCC5fzu2ovn4OH4X3z7+5d0P1apVS23bti3wIK+iD/6qiqp4zNSvX78CjyPyyns8Y98XF7Z8aV9T7du3L/D16qpWrVrlq1/Dhg3NK90AV1Tm8ZGAXAFy3xSquqlZs6YGDRqkmjVrOp0fGBhohq2oqCgFBASY8+xnlqOiohQYGCjp+qA+bNgwh3XYz/rUqlVLgwYNMssGBgaa261Vq1a+UJdb3jPYuetlr5t9vbm3Zw/IueuXuz25p+euW976FLSOoua5qjTrcLasO+oEAAWx3yeiKI0aNSr1Ogq7A/+IESOcjnV590O5H+f9qkl1+eqJu9x3330Vun2LxaImTZq4VDb38z5o0CDz+MBiseT70Lhu3bqF7judXUlnP/tbr149h+l5Hxenns4eu+KPf/yjQ/vK48o/oLwQkCuAqzvp8uTn5yc/P79Sr6ew77D6+PioX79+CggIUL9+/cxP4r29veXj4yN/f3/17dvXvLTOarWqf//+8vHxkcViMQOz1WpV3759FRQUpHvuuUe1a9c2P4X18fFRVFSUOS8gIMAs27dvX/Xr18+cV9glfAEBAQ7rzF0ve93s6829PavVmq9+uduTe3ruuuWtT0HrKGqeq0qzDmfLuqNOAFCQESNGuFSusAP9vOuw7/P8/PzMfVdoaKi6devmMG63atVK0vWzZnXq1HE61tWuXduhXO3atc3tjBw50mG7eR+jcDfeeGOFbLdHjx7msccjjzxSZPnAwECHD2gCAgIUFRVlriPv8/74448Xuu/MfRwiXQ+hHTt2lCQ99thjDuvK+7gohb1eXZW3fblPaACuynu2uDKcPZYki1HKu3gkJCQUejMli8Wi0NBQjR8/XtL1S6XyXqLkqqysLPP3EJsPfVZe3s4Pwm052Tq59NNilStNvYordzuKu12LxaL69evr119/rdI3YKlM6FP3c6VPrVar6tatW841K19FjY92xRkTyuv16up4WxZcHcPLsx7luY8oDU8Zz0qzn8ytKveHp4+Rro6PCQkJmjdvniTpiSeeqLJf36nKr8XcaEfl4gntKEkbynp85AwyAAAAAAAiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSJO/y2IiPj4/Gjh0rwzBktVrLY5OVmtVq1ZgxY8y/AVRvjAmAI94TsPPz83P6NwCUlXIJyBaLRT4+PjIMozw2V+nZ+wMAJMYEIC/eE7Dz8vJy+jcAlBVGGgAAAAAAREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQREAGAAAAAEASARkAAAAAAEkEZAAAAAAAJBGQAQAAAACQJHlXdAVKypaT7dI8V8sBAJwr77HS1TG8POsBAACqhyobkE+v+MKt5QAAzlXkOMoYDgAAyhOXWAMAAKBSslqtTv8GgLJSpc4gW61WjRkzpshyhmEoJydHkuTt7S2LxeLSugEA17k63paF4ozhFotFYWFhunjxogzDKLM6sY8AKobVatXYsWMVFham33//vaKrA6AaqFIB2WKxyMfHx6Wyvr6+ZVwbAPBcxRlvy4KrY7jFYpGvr698fHzKNCADqBj2scjX11cWi4X3OYAyxyXWAAAAAACIgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACBJ8i71CrxdW4Wr5eAa+tP96FP3K6xPq0N/l2Ubq0P/FQf94Yj+cFQV+6Mq1rk4StI+T+gTT2iDRDsqG09oR3HaUNbttRiGYZTpFgAAAAAAqALK/BLr9PR0jR8/Xunp6WW9qWqB/nQ/+tT96NOyQ986oj8c0R+O6A/P4AnPoye0QaIdlY0ntKMytqHMA7JhGDp9+rQ4Ue0e9Kf70afuR5+WHfrWEf3hiP5wRH94Bk94Hj2hDRLtqGw8oR2VsQ3cpAsAAAAAABGQAQAAAACQVA4B2Wq1avDgwbJarWW9qWqB/nQ/+tT96NOyQ986oj8c0R+O6A/P4AnPoye0QaIdlY0ntKMytoG7WAMAAAAAIC6xBgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkCR5u2Ml69ev11dffaWkpCSFh4friSeeUIsWLQosv3v3bi1ZskQJCQkKCwvT8OHD1aVLF3dUxSMUpz+3bt2qTz/91GGa1WrVv//97/KoapXw888/a82aNTp9+rQuX76sl156Sbfcckuhy8TExGjBggU6d+6cbrjhBj300EPq1atX+VS4kituf8bExGjSpEn5pv/zn/9UcHBwGda06lq5cqX27t2ruLg4+fj4KDIyUiNGjFCDBg3MMllZWVqwYIF27dql7OxsdezYUaNGjaoWfbpq1SotWrRI9913nx577DFJ1a8/EhMTtXDhQv3000/KzMxUWFiYnn32WTVv3lySZBiGli5dqi1btig1NVWtW7fWqFGjVL9+/QquufvZbDYtXbpU27dvV1JSkkJDQ9WzZ0899NBDslgskqpXf3ia4h5jupO7xuJLly7piy++UExMjPz8/NSzZ08NGzZMNWrUMMu4ctzhjr4o6fhZGdrgjnEvJSVFc+fO1f79+2WxWNS9e3c9/vjj8vPzM8ucOXNGc+bM0cmTJxUUFKT+/ftr4MCBDnUpaZZx13hV3u0o6tivMtXZHeN9qc8g79q1SwsWLNDgwYM1depUhYeHa/LkyUpOTnZa/tixY5o5c6buvvtuTZ06Vd26ddP777+vs2fPlrYqHqG4/SlJ/v7++uc//2n+mzVrVjnWuPLLzMxURESERo4c6VL5+Ph4vffee2rbtq2mTZumAQMG6LPPPtNPP/1UthWtIorbn3YzZsxweJ0GBQWVUQ2rvp9//ln9+vXT5MmTNWHCBF27dk3vvPOOMjIyzDLz58/X/v379eKLL2rSpEm6fPmyPvzwwwqsdfk4ceKENm3apPDwcIfp1ak/UlJS9Prrr8vb21t///vfNX36dD366KOqWbOmWWb16tVat26dnnzySU2ZMkW+vr6aPHmysrKyKrDmZWPVqlXatGmTRo4cqenTp2v48OFas2aN1q1bZ5apTv3hSUpyTORO7hiLbTab3n33XeXk5Oidd97R6NGjtXXrVi1ZssQs48pxhzv6oqTjZ2Vog7vGvY8//ljnzp3ThAkT9Morr+jIkSP6/PPPzflpaWl65513VKdOHb333nsaMWKEli1bps2bN5tlSpNl3DVelXc7ijr2q0x1dst4b5TSq6++asyePdt8fO3aNeOvf/2rsXLlSqflP/roI+Pdd991mPb3v//d+Pzzz0tbFY9Q3P787rvvjL/85S/lUzkPMGTIEGPPnj2FlvnXv/5lvPjiiw7Tpk+fbrzzzjtlWbUqyZX+PHz4sDFkyBAjJSWlnGrleZKTk40hQ4YYMTExhmEYRmpqqvHII48Yu3fvNsucP3/eGDJkiHHs2LGKqmaZS09PN55//nnjwIEDxptvvmnMmzfPMIzq1x8LFy40Xn/99QLn22w248knnzRWr15tTktNTTWGDRtm7NixozyqWK7effdd49NPP3WY9v777xszZ840DKP69YcnKe4xUVkryVj8ww8/GEOHDjUuX75sltmwYYPx6KOPGtnZ2YZhuHbcUdq+KM34WRna4I5x79y5c8aQIUOMEydOmGV+/PFHY+jQocbvv/9utuuxxx4z22Xf9gsvvGA+Lk2Wccd4VdHtyHvsV5nq7K7xvlRnkHNycnTq1Cm1b9/enObl5aX27dvr+PHjTpc5fvy4Q3lJ6tixo3755ZfSVMUjlKQ/JSkjI0PPPvusnnnmGU2bNk3nzp0rj+p6rF9++cXpa7Sw5wBFGzdunP7617/q7bff1tGjRyu6OlVKWlqaJCkwMFCSdOrUKV27ds3hddqwYUPVqVPHo1+ns2fPVufOndWhQweH6dWtP/773/+qWbNm+uijjzRq1CiNGzfO4RP2+Ph4JSUlOfRTQECAWrRo4ZH9ERkZqcOHD+vChQuSpNjYWB07dkydO3eWVP36w1OU9JioLJVkLD5+/LiaNGnicLlyp06dlJ6ebh6vFXXc4Y6+KM34WRna4I5x7/jx46pZs6Z5SbYktW/fXhaLRSdOnDDLtGnTRt7e//sWaseOHXXhwgWlpKSYZUqaZdwxXlWGduRWmersrvG+VN9BvnLlimw2W77veAUHB5tPfF5JSUmqXbu2w7TatWsrKSmpNFXxCCXpzwYNGuiZZ55ReHi40tLStGbNGk2YMEEfffSRbrjhhnKotecp6DWanp6urKws+fj4VFDNqqaQkBA9+eSTat68ubKzs7VlyxZNmjRJkydPVrNmzSq6epWezWbTl19+qVatWqlJkyaSrr9Gvb29HS4tkzx7LN25c6dOnz6td999N9+86tYf8fHx2rRpkwYMGKBBgwbp5MmTmjdvnry9vdWrVy+zzdVlX/vggw8qPT1dY8eOlZeXl2w2mx555BHdcccdklTt+sNTlOSYqCyVdCxOSkrK1wb7azF3mcKOO1JSUkrVF6UdPytDG9wx7iUlJeX7eleNGjUUGBjoUKZevXr56mifZy9b0vHEHeNVZWhHbpWpzu4a791yky5UnMjISEVGRjo8Hjt2rDZt2qRHHnmkAmsGXNegQQOHG5q0atVKv/32m9auXav/+7//q8CaVQ1z5szRuXPn9NZbb1V0VSrMpUuX9OWXX2rChAl8QKXrB+rNmzfXsGHDJElNmzbV2bNntWnTpmp5M8Hdu3drx44dev7559W4cWPFxsbqyy+/VEhISLXsD5SNqjoWe8r46SnjHuNV1VCqgBwUFCQvL698idzZJ012wcHB+b6Mn5yc7LF3Gi2OkvRnXt7e3mratKkuXrzo/gpWEwW9Rv39/av0zqUyadGiBZdZu2DOnDn64YcfNGnSJIcrQoKDg5WTk6PU1FSHT/09dSw9deqUkpOTNX78eHOazWbTkSNHtH79er322mvVqj9CQkLUqFEjh2mNGjXSnj17JP3vE/fk5GSFhISYZZKTkxUREVFe1Sw3Cxcu1MCBA3XbbbdJkpo0aaKEhAStWrVKvXr1qnb94SnccUzkLqUZi4ODg81LSHPPt8+z/1/YcUdp+sId42dFt0Fyz7gXHBysK1euOKzj2rVrSklJcWiHszq60lZX2uGO8aoytCO3ylRnd433pfoOsre3t5o1a6bDhw+b02w2mw4fPuxwVjO3yMhIHTp0yGHawYMH1bJly9JUxSOUpD/zstlsOnv2rMOLAsXTsmVLp69RV58DFC02NpbXaCEMw9CcOXO0d+9evfHGG/kuOWrWrJlq1Kjh8Dq9cOGCLl265JGv0/bt2+uDDz7QtGnTzH/NmzfX7bffbv5dnfqjVatW+S5JvHDhgurWrStJqlevnoKDgx36Iy0tTSdOnPDI/sjMzJSXl+PhjJeXlwzDkFT9+sNTuOOYqLTcMRZHRkbq7NmzDgf1Bw8elL+/vxn4ijruKE1fuGP8rOg2SO4Z9yIjI5WamqpTp06ZZQ4fPizDMMyfmoqMjNSRI0eUk5Pj0I4GDRqY3z0vTZZxx3hVGdqRW2Wqs7vG+1L/zNP999+vLVu2aOvWrTp//rxmz56tzMxM8zKBTz75RIsWLTLL33fffTpw4IC++uorxcXFaenSpTp58qT69+9f2qp4hOL25/Lly3XgwAH99ttvOnXqlD7++GMlJCSod+/eFdSCyicjI0OxsbGKjY2VdP17LLGxsbp06ZIkadGiRfrkk0/M8n379lV8fLwWLlyouLg4bdiwQbt379aAAQMqovqVTnH7c+3atdq3b58uXryos2fP6ssvv9Thw4fVr1+/iqh+lTBnzhxt375dL7zwgvz9/ZWUlKSkpCTzJwoCAgJ09913a8GCBTp8+LBOnTqlTz/9NN9XLjyFv7+/mjRp4vDP19dXtWrVUpMmTapdfwwYMEC//PKLVqxYoYsXL2rHjh3asmWL+Z6yWCy67777tGLFCv33v//V2bNn9cknnygkJETdunWr4Nq7X9euXbVixQr98MMPio+P1969e/X111+bba1u/eFJijomKmvuGIs7duyoRo0a6ZNPPlFsbKx++uknLV68WP369ZPVapXk2nFHSfvCHeNnRbdBcs+416hRI3Xq1Emff/65Tpw4oaNHj2ru3Lm69dZbFRoaKkm6/fbb5e3trc8++0znzp3Trl27tG7dOt1///1mXUqTZdwxXlVEOwo79qtMdXbXeG8x7B9ZlML69eu1Zs0aJSUlKSIiQo8//riZ5CdOnKi6detq9OjRZvndu3dr8eLFSkhIUP369V3+ce3qojj9+eWXX2rv3r1KSkpSzZo11axZMz3yyCNq2rRpRTahUomJidGkSZPyTe/Zs6dGjx6tWbNmKSEhQRMnTnRYZv78+Tp//nyBP3ZfXRW3P1evXq3NmzcrMTFRvr6+Cg8P10MPPaR27dqVc82rjqFDhzqd/uyzz5qvw6ysLC1YsEA7d+5UTk6OOnbsqFGjRnnkJcXOTJw4UREREXrsscckVb/+2L9/vxYtWqSLFy+qXr16GjBggPr06WPONwxDS5cu1ebNm5WWlqbWrVtr5MiRDvcD8BTp6elasmSJ9u7dq+TkZIWGhuq2227T4MGDzbuhVqf+8DSFHROVNXeNxQkJCZo9e7ZiYmLk6+urnj17avjw4apRo4ZZxpXjDnf1RUnGz8rQBneMeykpKZozZ472798vi8Wi7t2764knnpCfn59Z5syZM5ozZ45OnjypWrVqqX///nrwwQcd6lLSLOOu8aq821HUsV9lqrM7xnu3BGQAAAAAAKq6Ul9iDQAAAACAJyAgAwAAAAAgAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyNXeiRMnNGHCBP35z3/W0KFDFRsb6/KyQ4cO1dKlS8uucqhwP//8s9577z099dRTGjp0qPbu3VvsdRiGoTVr1uiFF17QsGHD9NRTT2nFihVlUFsAAFDRZs2apdGjR7t1nVu3btXQoUMVHx/v1vW6YvTo0Zo1a1a5bxcVx7uiK4CKk5OTo+nTp8tqteovf/mLfHx8VKdOnYquVoHOnz+vXbt2qVevXqpXr15FV6dayMzMVEREhO6++2598MEHJVrHvHnzdPDgQf35z39WkyZNlJKSopSUFDfXFAAAVEaZmZlavXq12rZtq7Zt21Z0dYAiEZCrsd9++00JCQl66qmn1Lt374quTpHOnz+v5cuXq23btgTkctK5c2d17ty5wPnZ2dn6//6//087d+5UWlqaGjdurOHDh5s7wPPnz2vTpk368MMP1aBBA0niuQMAwIM99dRTMgzDfJyZmanly5dLUpUMyDNmzJDFYqnoaqAcEZCrseTkZElSzZo1K7gmqKrmzJmjuLg4jRkzRiEhIdq7d6+mTJmiDz74QPXr19f+/ftVr1497d+/X5MnT5YktW/fXiNGjFBgYGAF1x4AALibt7dnxQur1VrRVUA586xXMFw2a9Ysff/995Kkjz76SJJ00003qW7duoqOjtbMmTM1e/ZsHTp0SD4+PurZs6dGjBghLy/nX1s/c+aMXn75ZY0bN04333yzJOnUqVN65ZVX1LRpU02dOtUsO2XKFKWkpGjKlCmSJJvNpuXLl2vLli1KTU1Vy5YtNXLkSL377ru66aabNHr0aG3dulWffvqpJGnSpEnmut58880q+WmkJ7h06ZL5vISGhkqSHnjgAR04cEDfffedhg0bpt9++02XLl1SdHS0nnvuOdlsNs2fP18ffvih3nzzzQpuAVB6CQkJWr16tQ4dOqRLly7J19dX7dq104gRI/JdLXHmzBnNnTtXJ06cUK1atXTPPfcoNDRU//jHP/TJJ584lP/xxx+1cuVKnT59WhaLRW3atNGIESPUuHHj8m4igCouMTFRS5Ys0U8//aSrV68qJCREnTp10uOPP66MjAytWLFCBw4cUHx8vLy8vNSqVSsNGzZMERER5jpiYmI0adIkjRkzRrGxsfruu++UkZGhdu3aaeTIkQ5f0Zs1a5Z+/vlnzZo1S/Hx8XruueckScuXLzfPJA8ePFhDhw7VmTNn9PXXX+vIkSO6fPmyAgIC1LlzZ/35z39WrVq1StTe3bt3a+nSpYqPj1dYWJgefvhh7du3z6yT3Zo1a7R3715duHBBmZmZatSokQYNGqSoqCiH9Y0ePdo8HpVkHvu89dZb2rNnj7Zt26asrCx16NBBTz31lIKCgkpUb1QeBORqyn5gtnLlSt17771q3ry5goODtX37dtlsNk2ePFktWrTQn//8Zx06dEhff/21wsLC1LdvX6fra9y4sWrWrKmff/7ZDMhHjhyRxWJRbGys0tLSFBAQIJvNpmPHjqlPnz7msosWLdKaNWvUtWtXdezYUWfOnNHkyZOVlZVllmnTpo3uvfderVu3ToMGDVLDhg0lyfwf5e/s2bOy2Wx64YUXHKbn5OSYZ4cNw1B2drZGjx5tXmL99NNP65VXXtGFCxfMaUBVdfLkSR07dky33XabQkNDlZCQoI0bN2rSpEn66KOP5OvrK+n6AeqkSZNksVg0aNAg+fr66ttvv3V6pmXbtm2aNWuWOnbsqOHDhyszM1MbN27UG2+8oalTp/I1BQAuS0xM1Kuvvqq0tDT17t1bDRs2VGJioqKjo5WZmanffvtN+/btU48ePVSvXj0lJSVp8+bNmjhxoj766CPzA3C7FStWyGKxaODAgbpy5YrWrl2rt99+W++//758fHzybT8oKEijRo3S7Nmzdcstt+iWW26RJIWHh0uSDh48qPj4ePXq1UvBwcE6f/68Nm/erPPnz2vy5MnFvrT5hx9+0IwZM9SkSRP96U9/Umpqqv7xj3/ka4ckrVu3Tl27dtXtt9+unJwc7dq1Sx999JFeeeUVdenSpchtzZs3TzVr1tSQIUMUHx+vb775RnPmzNHYsWOLVWdUPgTkaioyMlLZ2dlauXKl2rRpY35atn37dmVnZ6tHjx4aPHiwJKlv374aP368vv322wIDsv0Tx6NHj5rTjhw5om7duum///2vjh8/rk6dOunMmTNKT09XmzZtJElJSUlau3atunXrppdfftlcdtmyZVq2bJn5+MYbb1SbNm20bt06dejQgbPGlUBGRoa8vLw0derUfFcW+Pn5SZJCQkJUo0YNhyDcqFEjSdfPQBOQUdV16dIl39mGrl27asKECdqzZ4/uvPNOSdKqVauUmpqqqVOnmmdl7rrrLj3//PMOy2ZkZGjevHm6++679dRTT5nTe/bsqTFjxmjlypUO0wGgMIsWLVJSUpKmTJmi5s2bm9MffvhhGYahJk2aaObMmQ778TvvvFNjx47Vt99+ax4L2qWkpGj69Ony9/eXJDVt2lTTp0/X5s2bdd999+Xbvp+fn6KiojR79mw1adLEHBPt+vXrpz/84Q8O01q2bKmZM2fq6NGj5vFicdobGhqqt99+2zwWad++vSZOnKi6des6lJ05c6ZDqO/fv7/Gjx+vr7/+2qWAHBgYqAkTJpgh3jAMrVu3zjwphKqLn3mCU3mDcOvWrfXbb78Vukzr1q11+vRpZWRkSJKOHTumLl26KCIiQkeOHJH0v7PKrVu3liQdPnxY165dU79+/RzWde+997qrKSgjERERstlsSk5OVlhYmMO/4OBgSVKrVq107do1Xbx40VzuwoULklSp75gOuCr3wVVOTo6uXr2qsLAw1axZU6dOnTLnHThwQJGRkQ6XLAYGBur22293WN/BgweVmpqq2267TVeuXDH/eXl5qWXLloqJiSnzNgHwDDabTfv27VPXrl0dwrGdxWKR1Wo1w7HNZtPVq1fl5+enBg0a6PTp0/mWufPOO81wLElRUVEKCQnRjz/+WKI65h5Ds7KydOXKFbVs2VKSnG6/MImJiTp79qzuvPNOMxxL179C2KRJk0K3nZKSorS0NLVp08bl7fbp08fhDHebNm1ks9mUkJBQrHqj8uEMMvKxWq35vj9Rs2ZNpaamFrpcmzZtdO3aNR0/flx16tRRcnKyWrdurXPnzplnlo8ePapGjRqZl+DaB5GwsDCHdQUGBnLzsEogIyPDIdzGx8crNjZWgYGBatCggW6//XZ98sknevTRR9W0aVNduXJFhw4dUnh4uLp06aL27duradOm+sc//qHHHntMhmFozpw56tChA2eP4RGysrK0cuVKbd26VYmJiQ53bk1LSzP/TkhIMA/6css79v3666+SpLfeesvp9nIfmAJAYa5cuaL09HSn4dDOZrPpm2++0caNGxUfHy+bzWbOc3Yzzfr16zs8tlgsCgsLK3EoTElJ0bJly7Rr1y7z5rF2ucdQV1y6dElS/nHVPi1v8N2/f79WrFih2NhYZWdnm9Ndvaw77wf99uPWoo6XUfkRkJFPQTfiKkrz5s1ltVp15MgR1alTR7Vr11aDBg3Upk0bbdy4UdnZ2Tpy5Ij5/RNUfidPnnS4KdqCBQskXb/cc/To0Xr22We1YsUKLViwQImJiQoKClLLli3VtWtXSddfS+PHj9fcuXP15ptvytfXV507d9ajjz5aIe0B3G3u3Ln67rvvNGDAAEVGRpqX1c2cOdMhLLvKvsxzzz1nXomRW40aNUpVXwDIbeXKlVqyZInuuusuPfzwwwoMDJTFYtH8+fNLNIYV1/Tp03Xs2DE98MADioiIkJ+fn2w2m6ZMmeIQ1t3tyJEjmjZtmtq0aaORI0eaXwnbunWrduzY4dI6CjpeLo9+Q9kiIMNtvL291aJFCx09elQ33HCDeRl169atlZ2dre3btys5Odnh+yT274NcvHjR4cYzV69e5RO4SqBt27ZaunRpgfO9vb01dOhQDR06tMAyoaGheumll8qiekCFi46OVs+ePR0+9MnKyso3ftWtW9fp11RyX6EhXb/fgiTVrl1bHTp0KIMaA6gugoKC5O/vr7NnzxZYJjo6Wm3bttUzzzzjMD01NdXpXaTtV7nYGYahixcvFnqWuqAzsikpKTp06JCGDh3q8F3nvNtwlf2Mbt5x1dm0PXv2yGq16rXXXnP4GaetW7eWaNvwLHwHGW7Vpk0b/fLLL4qJiTGDcFBQkBo2bKjVq1ebZezatWunGjVqaOPGjQ7rWb9+fb51279PQnAGUFk4O4Owfv36fGc+OnbsqOPHjys2NtaclpKSku9MRceOHeXv76+VK1cqJycn37qvXLninooD8HheXl7q1q2b9u/fr5MnT+abbxiG0zFs9+7dSkxMdLrObdu2KT093XwcHR2ty5cvq3PnzgXWw343/7yXTNu3nfeM69q1awtcV26XL19WXFycOVaGhoaqcePG2rZtm3k/HEn6+eef831I4OXlJYvF4jBWx8fHa9++fS5tG56NM8jVmJ+fn5o2bepwp706deqYt97PLTg4WE2bNnWY1rRp03yXAHbu3Nm8UUPusx+333679u7dqy5duuiGG25wWO/w4cO1fft2ffnll4qMjNSvv/6qX375RW3btnW442Dz5s3VrFkz7du3T15eXvL29lbz5s1L/Dt5AFBa/fr1048//qg1a9boxhtv1JkzZ3TixAm1bdvW4ftpgwYN0tmzZzV//nzddttt8vHx0b59+9SxY0dduHDBPIMREBCgMWPGaMmSJfr444/VqVMn1axZU0lJSTp69KjCw8P14IMPVlBrAVQ1w4YN08GDBzVx4kT17t1bjRo10uXLlxUdHa233npLXbt21fLly/Xpp58qMjJSZ8+e1Y4dO8yrWfIKDAzUG2+8oV69eik5OVlr165VWFiYevfuXWAdfHx81KhRI+3atUv169dXYGCgGjdurCZNmqhNmzZas2aNrl27ptDQUPP3mF2xaNEiff/99w6/I/+nP/1J77//vl5//XX16tVLqampWr9+vRo3buwQmrt06aKvv/5aU6ZMMW+KuGHDBoWFhenMmTPF6GF4IgJyNda8eXNNnTrVYdrDDz+shx9+OF/Zvn375ruzdd5lpet3LXY2/aGHHtJDDz3ktB7333+/7r///iLrW6tWLb333ntFlgOA8pJ3zOzevbvTciEhIZo4caLDtLw/d2LXuXNnp2dj8t7tHwCKEhoaqilTpmjx4sXasWOH0tPTFRoaqk6dOsnX11eDBg1SRkaGdu7cqV27dqlp06Z65ZVXtGjRIqfrGzRokM6cOaNVq1YpPT1d7du316hRo8yzxAV5+umnNXfuXM2fP185OTkaPHiwmjRpohdeeEFz587Vhg0bZBiGOnTooL///e8l/jm7m2++WS+88IKWLVumRYsWKSwsTM8++6y+//57nT9/3izXrl07Pf3001q9erXmz5+vevXqafjw4YqPjycgQxajlN8kv3z5stPLwJypW7eux9/6nDZ6jurQzopso7e3t0JCQipk2+XFlfHRk15nntIWT2mHRFsqK1faUh3GSFQdMTExmjRpkl588cV8v/1eFbz88ssKCgrS66+/XtFVQRVQ6jPIOTk5DrdGL4j9C/o5OTkee3c32ug5qkM7q0MbK1pR46MnPQee0hZPaYdEWyorT2oLUNnk5OTIYrE43PE/JiZGZ86c0SOPPFKBNUNVwiXWAAAAAKq8xMREvf3227rjjjsUGhqquLg4bdq0ScHBwbrnnnsqunqoIgjIAAAAAKq8wMBANWvWTN9++62uXLkiX19fdenSRcOGDeOmrnAZARkAAABAgdq2baulS5dWdDWKFBAQoLFjx1Z0NVDF8TvIAAAAAACIgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACBJ8i6PjRiGoezsbGVmZsowjPLYJAAAxWKz2ZSRkaGrV68qMzOz2MtbrVZZLJYyqBkAACgv5RKQs7OzNWPGDEnS2LFjZbVay2OzAAC4LD09XbNmzSrx8mPGjJGPj48bawQAAMpbuVxinZ2d7fRvAAAAAAAqi3I5gwwAQFUS/sBj8vYLKLKcLSdbp1d8UQ41AgAA5YGADABAHl7eVnl583UgAACqG+5iDQAAAACACMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSJO/y2IjNZnP6NwAA7mAYhrKzsyVJVqtVFoulgmtUPFW9/gAAeIpyOYOckZHh9G8AANwhOztbM2bM0IwZM8ygWZVU9foDAOApuMQaAAAAAAARkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACQRkAEAAAAAkERABgAAAABAEgEZAAAAAABJBGQAAAAAACRJ3hVdgdxOnDihzZs3q0+fPtqzZ4/i4uLUsGFDDR8+3KHc6tWrdezYMbVq1UoDBw7Mt2yLFi2K3Na0adMcHudeV17bt29XdHS0oqKidMcdd+jTTz9VSkqKAgMD9eyzzzqU6dOnj7p06eKw/Mcff6yMjAzVqFFDNpvNXE9RbbIv58y4cePytSEoKEhXrlwpsu3OhIaGKjExscD5rqz7j3/8o2JiYnTs2LEit+ft7a0HHnhAK1asKLJs7j7J3eaGDRsW+Bqxy/vcffjhh7p27Zpq1Kihv/3tbwVuc9u2bdqzZ4+6d++e77mqaHnbVNC0wsqXheK+B+Fo+vTpys7OLtNt1KhRQ9euXSvTbZSFwMBApaSkOEwrasyqymbMmFGq5Z31TY8ePRQdHS3DMMxpXl5estlsRa4v91gbFxdnTh83bpwkx3HZ2TSLxaKoqCjVr19fmzdvdtiXtGrVSseOHTNfm4GBgcrMzFR2drasVqvGjh0rSZo6dWq+ZRo2bKirV686jDl594sF1aF+/fo6fvy4oqKiJClf3xS2Del/413u9Rw7dkyJiYny8/NTZmamoqKi8q1XksaPH19knwMAKk6lCcjZ2dnauHGjUlJStH79eqWlpUmS4uLidOnSJdWpU0eSlJycbAawY8eOKTk5WQEBAeayGzduVHh4uKxWa4Hb+uGHH/JNs6+rdu3aDtPT0tLMHVx0dLTq169vHqilpKTo/PnzCg0NNcts3rxZrVu3lr+/vyQpNjbWDLn2A9Po6Gh17dpVAQEBBbbp8uXLBYZjyflBQEnDsaQiDzRdWfdXX33l8gF+Tk6OVq1a5VJZe58cOHDAYbr9QC3va8Qu73MXFBRkPgfXrl3T0aNH1bp163zby7tc7ueqojmrm6QC61tebcn9/nXlPQhHJ06cKPNwLKlKhmNJ+cKxVPSYVZ0565vdu3fnm+ZKOJYcx9rcfvjhB23evNlhmrN9k338CQgIUGpqqsM8+77P/trM/VxnZ2frxIkT+T5ItS9jr499zJkzZ06BbbDXwd/fX2lpaeY+bffu3bJYLPlCbEHbsFqtDuNd7vXY2ffdzvpcuh72S/shCACg7FSaS6yjo6PNHaM9HNstXLjQ/HvRokUO8xYtWuSwbEpKiqKjowvdVt4dekHrlqSVK1eaO07DMPLtqBctWpSvzMqVK835S5cuzbfOvGWctcnZcpVdcQ/wXT04k/73PBck92vELu/zsmHDBof5a9ascbquwp7PiuasboXVt7zaUtz3IBz95z//qegqAMVW0L7UGcMw8oVjV7hylZF9zCnqg1zDMPIdX9inu7oNyXG8AwB4nkoRkC9fvqw9e/YUOD8rK0vR0dE6dOiQrl696jDv6tWr+T6l3bNnjy5fvux0Xf/85z8L3M7Vq1d16NAh83FsbGy+T8ydyVvm/Pnzio2NLfQMaVxcnGJjYwtsExwV1Sf214idq8/dv//9b4fHzpazP1cVraC6FVTf8mqLs/dvYe9BOPr8888rugoeJzs7W1lZWcX+l/tDPldCU95yJd1u3m2j+Ao6W+tOe/bsUWxsbKHHK64aM2ZM6SsEACgTLl9inZ2d7bADt1gs5mXEpWEYhjZt2lTkwci2bduKvc4hQ4bIYrGY01NSUpSUlFToshs2bNBNN90ki8VS4BlGV6xZs6bQS6RdLQPXbdu2TTfffLO8vLxcfu7i4uKUnp4uf39/2Wy2Apdbs2aNnnvuOXl5VcxnSoXVzZk1a9YUeIbe3pYaNWqUul4FvX8Leg96qpKOjxkZGTpy5EhZVq1amjVrVqnXYVzLKXY5d2wXlZd9HC7O1U+FWbVqVYH3PgEAVByXA/LKlSu1fPly83HTpk0dbppRUomJiW4/o2UYhmJjY5WYmKgbbrjBnD5//vwil7XZbDp48KCCgoJKFV5dWZZw7H67d+9WgwYNitW3ixcv1uOPP67Tp08XuFxGRoZOnz6t5s2bu6uqxVJY3ZwprKy9Le64kVZB79+C3oOeqqTj47Jly8qyWgDczJ37bVduaAkAKH8uB+RBgwbp/vvvNx+766xQaGioIiIidObMGZcvaSuKxWJRRESEQkNDHab/5S9/0aefflrosl5eXurQoYMsFov8/PxKvDN0ZdnSrB/O9ejRQ15eXsXq20ceeUTS9VBT0HL+/v5q2rSpW+taHIXVzRk/Pz/ZbDZlZWXlm+fOthT0/i3oPeipSjo+DhkyRDNnziyralVrJbmENS0tzfwajqWGa7vH3OVGjx5d4pvTnT9/3uFDFlRO7txvt2rVyi3rAQC4l8vXi1qtVgUEBJj/3HF5tXT9QPKee+4p8oCyV69euvfee11eZ58+ffKtMzAwUMHBwYUue++996pGjRry8vLSAw884NL2nBk4cKAiIyOLLONqm1C0Xr16ydvbu1jPXePGjc3XcmHLPfDAAxV2ebVUeN2cGThwoB588EGn89zZloLevwW9Bz1VScdHPz8/tWnTpoxrV/2MGTNGPj4+xf6XO9y6+trNXc5qtZZouz4+PmrUqJHb+wHu5eXlpYEDB7pt/CxojAYAVKxKcZOukJAQde/evcD5Pj4+uuWWW9S+fXvVqlXLYV6tWrXUo0cPh2ndu3dXSEiI03X99a9/LXA7tWrVUtu2bc3HERERatiwYZH1z1umUaNGCg8PL3Tn17BhQ4WHhxfYJjgqqk/srxE7V5+7P/3pTw6PnS1nf64qWkF1K6i+5dUWZ+/fwt6DcPTUU09VdBWAKi/vcUBZ6N69u8LDwws9XnEVP/MEAJVXpQjIkhQVFaXAwEBJyvc7rSNGjDD/HjZsmMO8YcOGOSxbq1YtRUVFFbqtPn36OJ2ed93S9Usn7WcILBaL/vjHP+ZbJm+ZQYMGmfOHDh2ab515yzhrk7PlKrviXlpYnJtE2Z/nguR+jdjlfV769evnML+gM7KFPZ8VzVndCqtvebWluO9BOHrooYcqugpAsRW0L3XGYrGoZs2axd5G3n2uM/YxJygoqMg6OPsdeFeuFsg9ruUe7wAAnqfSBGSr1aq+ffsqKChI/fv3N898NWzYUHXq1DHL1a5d2/zeTqtWrVS7dm2HZe+5554ig1qXLl3yTbOvK6+AgABFRUXJYrEoKipKLVq0MHeMgYGBatSokUOZPn36OOyAIyIi5OfnJ+l6ILSvJ3cZZ23KvZwz48aNyzetqIODwhT1XVFX1v2HP/zB5e9UeXt7u3z3Tnuf3HnnnQ7TC3qN2OV97jp27GiG8ho1aqh169ZOt2dfzsvLK99zVdHytsl+SW/eaYWVLwvFfQ/CUYsWLcqlz9xx5/KK4CyMVJfvt5eEs77p0aNHviDo6qXCucfa3Lp06ZJvXzRu3Lh80+zjT79+/fLtS+z7DPtrMzAw0HwvWK1WtWjRIt/67Ms0bNjQYcx5+umnC2yDvQ79+/dXUFCQWrVqJYvFoh49ephjpCvbsNfLPt7lXo+93/38/MxpzsL3+PHjC6wnAKDiWYxS3hkrISGhyN9vTEhI0Lx58yRJTzzxhNMw4wksFovq16+vX3/91W03HKtsqkMbperRzopuo9VqVd26dct9u+WpqPGxop8Dd6rotmRlZZmXrdq/g1xcqamp5k81Nf3jk/L2K/oDJVtOtk4u/bRU25XcU/+8Kvo5cafq2JbqMEYCQGVUac4gAwAAAABQkQjIAAAAAACIgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkgjIAAAAAABIIiADAAAAACCJgAwAAAAAgCQCMgAAAAAAkiTv8tiIn5+f078BAHAHq9WqMWPGmH9XNVW9/gAAeIpyCcheXl5O/wYAwB0sFot8fHwquholVtXrDwCApyCtAgAAAAAgAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYAMAAAAAIAkybuiKwAAQGVjy8mWLSfbpXIAAMBzEJABAMjjzJovK7oKAACgApTLJdZWq9Xp3wAAAAAAVBblcgbZarVq7NixCgsL0++//14emwQAoFj8/f313HPP6cYbbyzRvooPgAEAqPrKJSBbLBb5+PjI19dXFotFhmGUx2YBAHCZl5eXatasqVq1aiklJYV9FQAA1RB3sQYAAAAAQARkAAAAAAAkEZABAAAAAJBEQAYAAAAAQBIBGQAAAAAASQRkAAAAAAAkEZABAAAAAJBEQAYAAAAAQBIBGQAAAAAASQRkAAAAAAAkEZABAAAAAJBEQAYAAAAAQBIBGQAAAAAASQRkAAAAAAAkEZABAAAAAJBEQAYAAAAAQBIBGQAAAAAASZJ3qVfgXbxVFLd8VUQbPUd1aGdFtZG+LX65qsBT2uIp7ZBoS2VVVFs8qa0AUJVYDMMwKroSAAAAAABUtHK7xDo9PV3jx49Xenp6eW2y3NFGz1Ed2lkd2ljZedJz4Clt8ZR2SLSlsvKktgCAJyq3gGwYhk6fPi1PPmFNGz1HdWhndWhjZedJz4GntMVT2iHRlsrKk9oCAJ6Im3QBAAAAACACMgAAAAAAksoxIFutVg0ePFhWq7W8NlnuaKPnqA7trA5trOw86TnwlLZ4Sjsk2lJZeVJbAMATcRdrAAAAAADEJdYAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSJO/y2tD69ev11VdfKSkpSeHh4XriiSfUokWL8tq8y37++WetWbNGp0+f1uXLl/XSSy/plltuMecbhqGlS5dqy5YtSk1NVevWrTVq1CjVr1/fLJOSkqK5c+dq//79slgs6t69ux5//HH5+fmZZc6cOaM5c+bo5MmTCgoKUv/+/TVw4MByaePKlSu1d+9excXFycfHR5GRkRoxYoQaNGhglsnKytKCBQu0a9cuZWdnq2PHjho1apSCg4PNMpcuXdIXX3yhmJgY+fn5qWfPnho2bJhq1KhhlomJidGCBQt07tw53XDDDXrooYfUq1evMm/jxo0btXHjRiUkJEiSGjVqpMGDB6tz584e0T5nVq1apUWLFum+++7TY489Jskz2+kpqsqYmJu7xo7KpqTvncoiMTFRCxcu1E8//aTMzEyFhYXp2WefVfPmzSW5tt+qDGw2m5YuXart27crKSlJoaGh6tmzpx566CFZLBZJlbct5XXsAAAoe+VyBnnXrl1asGCBBg8erKlTpyo8PFyTJ09WcnJyeWy+WDIzMxUREaGRI0c6nb969WqtW7dOTz75pKZMmSJfX19NnjxZWVlZZpmPP/5Y586d04QJE/TKK6/oyJEj+vzzz835aWlpeuedd1SnTh299957GjFihJYtW6bNmzeXefuk6zvyfv36afLkyZowYYKuXbumd955RxkZGWaZ+fPna//+/XrxxRc1adIkXb58WR9++KE532az6d1331VOTo7eeecdjR49Wlu3btWSJUvMMvHx8XrvvffUtm1bTZs2TQMGDNBnn32mn376qczbGBoaqmHDhum9997Tu+++q3bt2mnatGk6d+6cR7QvrxMnTmjTpk0KDw93mO5p7fQUVWlMzM0dY0dlU9L3TmWRkpKi119/Xd7e3vr73/+u6dOn69FHH1XNmjXNMq7styqDVatWadOmTRo5cqSmT5+u4cOHa82aNVq3bp1ZprK2pTyOHQAA5cQoB6+++qoxe/Zs8/G1a9eMv/71r8bKlSvLY/MlNmTIEGPPnj3mY5vNZjz55JPG6tWrzWmpqanGsGHDjB07dhiGYRjnzp0zhgwZYpw4ccIs8+OPPxpDhw41fv/9d8MwDGPDhg3GY489ZmRnZ5tlFi5caLzwwgtl3CLnkpOTjSFDhhgxMTGGYVxv0yOPPGLs3r3bLHP+/HljyJAhxrFjxwzDMIwffvjBGDp0qHH58mWzzIYNG4xHH33UbNe//vUv48UXX3TY1vTp04133nmnjFvk3GOPPWZs2bLF49qXnp5uPP/888aBAweMN99805g3b55hGJ77PHqCqjom5lWSsaMyKc17p7JYuHCh8frrrxc435X9VmXx7rvvGp9++qnDtPfff9+YOXOmYRhVpy1ldewAACgfZX4GOScnR6dOnVL79u3NaV5eXmrfvr2OHz9e1pt3q/j4eCUlJalDhw7mtICAALVo0cJsy/Hjx1WzZk3z0jZJat++vSwWi06cOGGWadOmjby9/3eFe8eOHXXhwgWlpKSUU2v+Jy0tTZIUGBgoSTp16pSuXbvm8Jw1bNhQderUcWhnkyZNHC437NSpk9LT082ztL/88ovDOqTr7Szv591ms2nnzp3KzMxUZGSkx7Vv9uzZ6ty5s8PrUvK859FTeNKYWJKxozIpzXunsvjvf/+rZs2a6aOPPtKoUaM0btw4h6uRXNlvVRaRkZE6fPiwLly4IEmKjY3VsWPHzK/GVKW25OauYwcAQPko8+8gX7lyRTabLd/3toKDg82dYFWRlJQkSapdu7bD9Nq1a5vzkpKSFBQU5DC/Ro0aCgwMdChTr149hzL2/klKSjIPNsuDzWbTl19+qVatWqlJkyZmHby9vR0u0ZPytzPvc2rvl9xlnPVVenq6srKy5OPj4/4G5XL27Fm99tprys7Olp+fn1566SU1atRIsbGxHtE+Sdq5c6dOnz6td999N988T3kePY2njIklHTsqi9K+dyqL+Ph4bdq0SQMGDNCgQYN08uRJzZs3T97e3urVq5dL+63K4sEHH1R6errGjh0rLy8v2Ww2PfLII7rjjjskubYProzcdewAACgf5XaTLlROc+bM0blz5/TWW29VdFXcrkGDBnr//feVlpam6OhozZo1S5MmTaroarnNpUuX9OWXX2rChAmEVJS7qjx2eNJ7x2azqXnz5ho2bJgkqWnTpjp79qw2bdpU5W6kt3v3bu3YsUPPP/+8GjdurNjYWH355ZcKCQmpcm0BAFRdZR6Qg4KC5OXlle8TUGdnrio7e32Tk5MVEhJiTk9OTlZERIRZ5sqVKw7LXbt2TSkpKebywcHBTvsj9zbKw5w5c/TDDz9o0qRJuuGGG8zpwcHBysnJUWpqqsMZlOTkZIc25L3sy36Dodxl8t50KDk5Wf7+/uVyUOrt7a2wsDBJUrNmzXTy5El98803uvXWWz2ifadOnVJycrLGjx9vTrPZbDpy5IjWr1+v1157zSPa6Wk8YUwszdhRGbjjvVNZhISEqFGjRg7TGjVqpD179khybb9VWSxcuFADBw7UbbfdJklq0qSJEhIStGrVKvXq1atKtSU3dx07AADKR5l/B9nb21vNmjXT4cOHzWk2m02HDx9WZGRkWW/ererVq6fg4GAdOnTInJaWlqYTJ06YbYmMjFRqaqpOnTplljl8+LAMwzB/wiUyMlJHjhxRTk6OWebgwYNq0KBBuVxebRiG5syZo7179+qNN97Id7l3s2bNVKNGDYd2XrhwQZcuXXJo59mzZx2C08GDB+Xv728erLVs2dJhHfYyFfW822w2ZWdne0z72rdvrw8++EDTpk0z/zVv3ly33367+bcntNPTVOUx0R1jR2XgjvdOZdGqVat8l+ZfuHBBdevWleTafquyyMzMlJeX42GJl5eXDMOQVLXakpu7jh0AAOWjXC6xvv/++zVr1iw1a9ZMLVq00DfffKPMzMxKeclURkaGLl68aD6Oj49XbGysAgMDVadOHd13331asWKF6tevr3r16mnx4sUKCQlRt27dJF3/5L5Tp076/PPP9eSTTyonJ0dz587VrbfeqtDQUEnS7bffrmXLlumzzz7TwIEDde7cOa1bt05/+ctfyqWNc+bM0Y4dOzRu3Dj5+/ubZ7ICAgLk4+OjgIAA3X333VqwYIECAwMVEBCguXPnKjIy0tyZd+zYUY0aNdInn3yi4cOHKykpSYsXL1a/fv1ktVolSX379tWGDRu0cOFC3XXXXTp8+LB2796tV155pczbuGjRInXq1El16tRRRkaGduzYoZ9//lmvvfaaR7RPkvz9/c3vftr5+vqqVq1a5nRPaKcnqkpjYm7uGDsqA3e8dyqLAQMG6PXXX9eKFSt066236sSJE9qyZYv++te/SpIsFkuR+63KomvXrlqxYoXq1Klj3i/i66+/1l133SWpcrelPI4dAADlw2LYP5otY+vXr9eaNWuUlJSkiIgIPf7442rZsmV5bLpYYmJinH5PtWfPnho9erQMw9DSpUu1efNmpaWlqXXr1ho5cqQaNGhglk1JSdGcOXO0f/9+WSwWde/eXU888YT8/PzMMmfOnNGcOXN08uRJ1apVS/3799eDDz5YHk3U0KFDnU5/9tlnzQP0rKwsLViwQDt37lROTo46duyoUaNGOVzqlZCQoNmzZysmJka+vr7q2bOnhg8frho1aphlYmJiNH/+fJ0/f1433HCDHnrooXIJAf/4xz90+PBhXb58WQEBAQoPD9fAgQPNu4hW9fYVZOLEiYqIiNBjjz0myXPb6QmqypiYm7vGjsqoJO+dymL//v1atGiRLl68qHr16mnAgAHq06ePOd+V/VZlkJ6eriVLlmjv3r1KTk5WaGiobrvtNg0ePNj81YfK2pbyOnYAAJS9cgvIAAAAAABUZmX+HWQAAAAAAKoCAjIAAAAAACIgAwAAAAAgiYAMAAAAAIAkAjIAAAAAAJIIyAAAAAAASCIgAwAAAAAgiYCMIsyaNUujR4926zq3bt2qoUOHKj4+vtByS5cu1dChQ926bQCVj/29fuXKlYquCtxk6NChmjNnTkVXAwCAYiMgo1gyMzO1dOlSxcTEVHRVAAAAAMCtvCu6AqjcnnrqKRmGYT7OzMzU8uXLJUlt27atqGoBAAAAgNtxBhmF8vb2ltVqrehqAEC5yMjIqOgquCQzM7OiqwAAgEfiDHIVk5iYqCVLluinn37S1atXFRISok6dOunxxx9XRkaGVqxYoQMHDig+Pl5eXl5q1aqVhg0bpoiICHMdMTExmjRpksaMGaPY2Fh99913ysjIULt27TRy5EjVqVPHLDtr1iz9/PPPmjVrluLj4/Xcc89JkpYvX26eSR48eLCGDh2qM2fO6Ouvv9aRI0d0+fJlBQQEqHPnzvrzn/+sWrVquaX9165d08qVK/X999/r999/V0hIiG677TYNGTLEIcifPHlSixcv1qlTp5SRkaHg4GC1bdtWzz77rFlm586dWrNmjX799VdZLBbVqVNHvXv31n333eeWugIonrS0NP3rX//Svn37ZBiGunfvrpEjR8rX11eS6+//oUOHmuNSbqNHj9ZNN91k3ldh69at+vTTTzVx4kTt2rVL0dHRunbtmubNm6f09HQtWbJE+/btM8ez8PBwDR8+XM2aNSuwDUuXLtXy5cs1ffp0LVmyRAcOHFCNGjV0xx13aPjw4fLx8XEov23bNq1du1bnz5+Xj4+POnbsqBEjRjiMwxMnTtTVq1c1evRozZ8/XydPnlSfPn302GOPOaxr/Pjxqlu3rl566SVz2t/+9jedO3dO77//vsLDwyVJu3bt0owZM/TRRx+pUaNGkq7vWxYvXqwff/xRqampCgsL0/3336+7777bYRvZ2dlauXKltm/frt9//121a9fWbbfdpocffrjID1P/85//aOnSpXrsscd07733FloWAICKQkCuQhITE/Xqq68qLS1NvXv3VsOGDZWYmKjo6GhlZmbqt99+0759+9SjRw/Vq1dPSUlJ2rx5syZOnKiPPvpIoaGhDutbsWKFLBaLBg4cqCtXrmjt2rV6++239f777+c7iJOkoKAgjRo1SrNnz9Ytt9yiW265RZLMg66DBw8qPj5evXr1UnBwsM6fP6/Nmzfr/Pnzmjx5siwWS6n74LPPPtP333+vqKgo3X///frll1+0atUqxcXF6eWXX5YkJScn65133lFQUJAGDhyomjVrKiEhQXv27DHXc/DgQc2cOVPt27fX8OHDJUlxcXE6evQoARmoINOnT1fdunU1bNgwnTp1St9++62CgoI0YsQISa69/0ti9uzZCgoK0uDBg80zyF988YWio6PVv39/NWrUSFevXtXRo0cVFxdXaEDO25Y//elP+uWXX7Ru3TqlpqaaHzJK18fgJUuWqEePHurdu7euXLmidevW6c0339S0adNUs2ZNs+zVq1c1ZcoU3XrrrbrjjjtUu3btfNts3bq1du7caT5OSUnR+fPnZbFYdOTIEXOsPnLkiIKCgsxwnJSUpNdee02S1K9fPwUFBemnn37SZ599pvT0dA0YMECSZLPZNG3aNB09elS9e/dWo0aNdPbsWa1du1YXLlzQuHHjCuyPxYsXa+XKlXryySfVp0+fIvsPAICKQkCuQhYtWqSkpCRNmTJFzZs3N6c//PDDMgxDTZo00cyZM+Xl9b8r5++8806NHTtW3377rQYPHuywvpSUFE2fPl3+/v6SpKZNm2r69OnavHmz05Do5+enqKgozZ49W02aNNGdd97pML9fv376wx/+4DCtZcuWmjlzpo4ePao2bdqUqv2xsbH6/vvvdffdd+vpp582t1m7dm199dVXOnz4sNq1a6djx44pNTVVEyZMcOinRx55xPz7hx9+kL+/v1577TWH/gJQcSIiIvTMM8+Yj1NSUvTdd99pxIgRLr//SyIwMFBvvPGGw1jwww8/qHfv3nr00UfNaQMHDnR5nfXq1TMDY//+/eXv76+NGzfqD3/4g8LDw5WQkKClS5fq4Ycf1h//+EdzuVtuuUXjx4/Xhg0bHKYnJSXpySef1D333FPgNtu0aaN169bp/PnzatSokY4ePSpvb2917NhRR48eVf/+/SVJR48eVevWrc3lFi9eLJvNpg8++MC82qdv376aMWOGli1bpnvuuUc+Pj7asWOHDh48qEmTJjks37hxY33xxRc6duyYWrVqla9eCxYs0Nq1a/XMM8+oV69eLvchAAAVgWRQRdhsNu3bt09du3Z1CH12FotFVqvVPMCz2Wy6evWq/Pz81KBBA50+fTrfMnfeeacZjiUpKipKISEh+vHHH0tUx9xnnbOysnTlyhW1bNlSkpxuv7js9br//vsdpttD+Q8//CBJ5lmX/fv3Kycnx+m6AgIClJmZqYMHD5a6XgDcI2/4a926ta5evaq0tDSX3/8l0bt373wflNWsWVMnTpxQYmJiidbZr18/h8f2S4rt7dizZ48Mw9Ctt96qK1eumP+Cg4MVFhaW75cCrFar7rrrrkK3aQ+tR44ckXQ9CDdv3lwdOnQwp6Wmpurs2bNmWcMwtGfPHnXt2lWGYTjUpVOnTkpLS9OpU6ckSdHR0WrUqJEaNGjgUM7+wUTeOhuGoTlz5mjdunX6v//7P8IxAKBK4AxyFXHlyhWlp6erSZMmBZax2Wz65ptvtHHjRsXHx8tms5nzAgMD85WvX7++w2OLxaKwsDAlJCSUqI4pKSlatmyZdu3apeTkZId5aWlpJVpnbgkJCWYdcwsODlbNmjV16dIlSdJNN92k7t27a/ny5Vq7dq3atm2rbt266fbbbze/I9evXz/t3r1bU6ZMUWhoqDp06KBbb71VnTp1KnU9AZRM7u/dSv8bt1JTU11+/5dEvXr18k0bPny4Zs2apWeeeUbNmjVT586d1bNnT914440urTPv+HrjjTfKYrGYv/9+8eJFGYah559/3uny3t6Ou+fQ0NB80/IKDg5W/fr1deTIEd1zzz06cuSI2rZtqzZt2mju3Ln67bffFBcXJ8MwzCt6rly5otTUVG3evFmbN292ul7771P/+uuviouL06hRo5yWyzvub9u2TRkZGRo1apRuv/32QusOAEBlQUD2ICtXrtSSJUt011136eGHH1ZgYKAsFovmz5/v8FNNZWX69Ok6duyYHnjgAUVERMjPz082m01TpkxxCOulVdR3mS0Wi/72t7/p+PHj2r9/vw4cOKB//OMf+vrrrzV58mT5+fmpdu3aev/99/XTTz/pp59+0o8//qitW7fqzjvvdPiOIIDyU9DXHXKPX6W5l0FB45Czey7ceuutatOmjfbu3asDBw7oq6++0urVq/XSSy+pc+fOxd523nrbbDZZLBa9+uqrTtvt5+dXZB2dadWqlQ4fPqysrCydOnVKgwcPVuPGjVWzZk0dOXJEcXFx8vPzU9OmTSX9r2/vuOMO9ezZ0+k67d9dtn+VJ/dl57nl/YCjVatWio2N1YYNG3Trrbc6/aAWAIDKhoBcRQQFBcnf319nz54tsEx0dLTatm3r8B0+6frZF2d3kf71118dHhuGoYsXLxZ6lrqgg9OUlBQdOnTIvHtsQdsojbp168owDP3666/mzWWk69/NS01NzXdwFhkZqcjISP3pT3/Sjh079PHHH2vnzp3q3bu3pOtnaG6++WbdfPPNstlsmj17tjZv3qzBgwfnO0sFoGIV5/1fs2bNfFet5OTk6PLly8XaZkhIiPr166d+/fopOTlZ48eP14oVK1wKyL/++qvDmWn7GWP7tLCwMPNxgwYNilWvwrRp00Zbt27Vzp07ZbPZ1KpVK/MXDew3GbNPk/63b7HZbOrQoUOh677xxht15swZtW/f3qUPKsLCwjRixAhNnDhRkydP1htvvOHwtR4AACojvoNcRXh5ealbt27av3+/Tp48mW++YRhOz0Ls3r27wO/Qbdu2Tenp6ebj6OhoXb58udCDP/vPreQ9+LRvO++Z6rVr1xa4rtwuX76suLi4Ar8zLMms1zfffOMw/euvv5YkdenSRdL1sJ63HvafucrOzpZ0/Y6weetvP0tiLwOg8nD1/S9dD2b279zabd682eUrWWw2W74xrnbt2goJCXEYo65cuaK4uDinv0m8YcMGh8fr1q2TJPNrHLfccou8vLy0fPnyfOOVYRj5xqi8cnJyFBcXly/02y+dXr16tcLDwxUQEGBOP3TokE6ePOlwgy0vLy91795de/bscfoBrP3yaknq0aOHEhMTtWXLlnzlsrKynP6GdHh4uF599VXFxcVp6tSpysrKKrRdAABUNM4gVyHDhg3TwYMHNXHiRPMnNi5fvqzo6Gi99dZb6tq1q5YvX65PP/1UkZGROnv2rHbs2FHgd+bsd27t1auXkpOTtXbtWoWFhZlnWJ3x8fFRo0aNtGvXLtWvX1+BgYFq3LixmjRpojZt2mjNmjW6du2aQkNDzd9jdsWiRYv0/fff65NPPnH6fUDpesjt2bOnNm/erNTUVN100006ceKEvv/+e3Xr1s28Ucz333+vjRs3qlu3bgoLC1N6erq2bNkif39/8yD6s88+U0pKitq1a6cbbrhBCQkJWr9+vSIiItSwYUOX6gyg/Lj6/peku+++W1988YU++OADdejQQWfOnNGBAwdc/j329PR0Pf3004qKilJ4eLj8/PzMcJn78uL169dr+fLlevPNN9W2bVuHdcTHx2vq1Knq1KmTjh8/ru3bt+v22283P6wLCwvTI488okWLFikhIUHdunWTn5+f4uPjtW/fPvXu3VsPPPBAgXVMTEzU2LFj1bNnT/N3ne3rDQ4O1oULF8y7VkvXA/K///1v8+/chg0bppiYGL322mvmviUlJUWnTp3SoUOHNG/ePEnXb+y4e/duffHFFzp8+LBat24tm82muLg47d69W6+99prTm0hGRkZq3Lhxevfdd/Xhhx/q5ZdfLvL71AAAVBT2UFVIaGiopkyZosWLF2vHjh1KT09XaGioOnXqJF9fXw0aNEgZGRnauXOndu3apaZNm+qVV17RokWLnK5v0KBBOnPmjFatWqX09HS1b99eo0aNMs8SF+Tpp5/W3LlzNX/+fOXk5Gjw4MFq0qSJXnjhBc2dO1cbNmyQYRjq0KGD/v73v+upp55yWx88/fTTuvHGG7V161bt3btXwcHBevDBBzVkyBCzjP3A2X6zsICAADVv3lzPP/+8Gb7vuOMObdmyRRs3blRqaqqCg4PVo0cPDR06lJ99AiopV97/0vW7UsfHx+vbb7/VgQMH1Lp1a02YMEFvv/22S9vx9fVVv379dODAAe3du1c2m01hYWEaNWqU+vbt69I6xowZo6VLl2rRokXy8vJS//79zd9ztnvwwQdVv359rV27VsuWLZN0/Xu8HTp00M033+zSdpxp3bq1oqOjHc4UN2vWTL6+vrp27ZpatGjhUD44OFhTpkzR8uXLtWfPHm3YsEG1atVS48aNzd+Jl66fbX755Ze1du1abdu2Tfv27ZOPj49uvPFG3XffffluTJZbu3btNHbsWH344Yf65JNP9PzzzzPWAgAqJYtRHndvQqUSExOjSZMm6cUXX1RUVFRFVwcAPMbSpUu1fPlyzZ49W0FBQRVdHQAAUEx8fAsAAAAAgAjIAAAAAABIIiADAAAAACCJ7yADAAAAACCJM8gAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEgiIAMAAAAAIImADAAAAACAJAIyAAAAAACSCMgAAAAAAEiS/n/I/NHFOyP68gAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clist = ['fnlwgt','age','capital.gain','capital.loss','hours.per.week']\n", + "plt.figure(figsize = (12,6))\n", + "for i in range(0, len(clist)):\n", + " plt.subplot(2,3, i+1)\n", + " sns.boxplot(ds[clist[i]], color = 'skyblue')\n", + "print(\"BoxPlots of the features:\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.082396, + "end_time": "2021-05-22T12:30:23.203090", + "exception": false, + "start_time": "2021-05-22T12:30:23.120694", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Outliers are present in the continous columns of the feature**. We will check the z-score of the features and and clip them from the data." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:23.376732Z", + "iopub.status.busy": "2021-05-22T12:30:23.376029Z", + "iopub.status.idle": "2021-05-22T12:30:23.407325Z", + "shell.execute_reply": "2021-05-22T12:30:23.407861Z", + "shell.execute_reply.started": "2021-05-22T11:33:22.220652Z" + }, + "papermill": { + "duration": 0.122692, + "end_time": "2021-05-22T12:30:23.408061", + "exception": false, + "start_time": "2021-05-22T12:30:23.285369", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 2566)\n" + ] + }, + { + "data": { + "text/html": [ + "
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agefnlwgteducation.numcapital.gaincapital.losshours.per.weekworkclass_encoccupation_enccountry_encsex_encmarital_status_encrace_encrelationship_encincome
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3256022201490900200.6970300.122729110.3226770.8562080.1473080
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28214 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " age fnlwgt education.num capital.gain capital.loss hours.per.week \\\n", + "1470 62 159908 13 0 1258 38 \n", + "1471 72 144515 16 0 1258 40 \n", + "1473 66 350498 10 0 1258 20 \n", + "1474 33 262042 9 0 1138 40 \n", + "1475 29 138190 9 0 1138 40 \n", + "... ... ... ... ... ... ... \n", + "32556 22 310152 10 0 0 40 \n", + "32557 27 257302 12 0 0 38 \n", + "32558 40 154374 9 0 0 40 \n", + "32559 58 151910 9 0 0 40 \n", + "32560 22 201490 9 0 0 20 \n", + "\n", + " workclass_enc occupation_enc country_enc sex_enc \\\n", + "1470 0.064279 0.122729 1 0 \n", + "1471 0.064279 0.132365 1 1 \n", + "1473 0.697030 0.051989 1 1 \n", + "1474 0.064279 0.122729 1 0 \n", + "1475 0.697030 0.118823 1 0 \n", + "... ... ... ... ... \n", + "32556 0.697030 0.021128 1 1 \n", + "32557 0.697030 0.030210 1 0 \n", + "32558 0.697030 0.065174 1 1 \n", + "32559 0.697030 0.122729 1 0 \n", + "32560 0.697030 0.122729 1 1 \n", + "\n", + " marital_status_enc race_enc relationship_enc income \n", + "1470 0.466795 0.856208 0.046715 0 \n", + "1471 0.466795 0.856208 0.413569 0 \n", + "1473 0.466795 0.856208 0.413569 0 \n", + "1474 0.138616 0.856208 0.147308 0 \n", + "1475 0.322677 0.094700 0.106485 0 \n", + "... ... ... ... ... \n", + "32556 0.322677 0.856208 0.256039 0 \n", + "32557 0.466795 0.856208 0.046715 0 \n", + "32558 0.466795 0.856208 0.413569 1 \n", + "32559 0.027346 0.856208 0.106485 0 \n", + "32560 0.322677 0.856208 0.147308 0 \n", + "\n", + "[28214 rows x 14 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import zscore\n", + "zabs = np.abs(zscore(new_ds.loc[:,'fnlwgt':'hours.per.week']))\n", + "print(np.shape(np.where(zabs >= 3)))\n", + "new_ds = new_ds[(zabs < 3).all(axis = 1)]\n", + "new_ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.084603, + "end_time": "2021-05-22T12:30:23.578382", + "exception": false, + "start_time": "2021-05-22T12:30:23.493779", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "WE have a total of 2566 outliers in the data. After removing the outliers, we have 28213 observations left.\n", + "## Correlation:\n", + "Checking the correlation between the features and target variable to see which of them columns are more related to target." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:23.768327Z", + "iopub.status.busy": "2021-05-22T12:30:23.767535Z", + "iopub.status.idle": "2021-05-22T12:30:24.986767Z", + "shell.execute_reply": "2021-05-22T12:30:24.987350Z", + "shell.execute_reply.started": "2021-05-22T11:34:36.955891Z" + }, + "papermill": { + "duration": 1.324769, + "end_time": "2021-05-22T12:30:24.987537", + "exception": false, + "start_time": "2021-05-22T12:30:23.662768", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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28214 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " age fnlwgt education.num capital.gain capital.loss \\\n", + "1470 0.616438 0.296903 0.769231 0.0 1.00000 \n", + "1471 0.753425 0.265630 1.000000 0.0 1.00000 \n", + "1473 0.671233 0.684115 0.538462 0.0 1.00000 \n", + "1474 0.219178 0.504404 0.461538 0.0 0.90461 \n", + "1475 0.164384 0.252780 0.461538 0.0 0.90461 \n", + "... ... ... ... ... ... \n", + "32556 0.068493 0.602146 0.538462 0.0 0.00000 \n", + "32557 0.136986 0.494774 0.692308 0.0 0.00000 \n", + "32558 0.315068 0.285660 0.461538 0.0 0.00000 \n", + "32559 0.561644 0.280654 0.461538 0.0 0.00000 \n", + "32560 0.068493 0.381383 0.461538 0.0 0.00000 \n", + "\n", + " hours.per.week workclass_enc occupation_enc country_enc sex_enc \\\n", + "1470 0.464789 0.064279 0.122729 1 0 \n", + "1471 0.492958 0.064279 0.132365 1 1 \n", + "1473 0.211268 0.697030 0.051989 1 1 \n", + "1474 0.492958 0.064279 0.122729 1 0 \n", + "1475 0.492958 0.697030 0.118823 1 0 \n", + "... ... ... ... ... ... \n", + "32556 0.492958 0.697030 0.021128 1 1 \n", + "32557 0.464789 0.697030 0.030210 1 0 \n", + "32558 0.492958 0.697030 0.065174 1 1 \n", + "32559 0.492958 0.697030 0.122729 1 0 \n", + "32560 0.211268 0.697030 0.122729 1 1 \n", + "\n", + " marital_status_enc race_enc relationship_enc income \n", + "1470 0.466795 0.856208 0.046715 0 \n", + "1471 0.466795 0.856208 0.413569 0 \n", + "1473 0.466795 0.856208 0.413569 0 \n", + "1474 0.138616 0.856208 0.147308 0 \n", + "1475 0.322677 0.094700 0.106485 0 \n", + "... ... ... ... ... \n", + "32556 0.322677 0.856208 0.256039 0 \n", + "32557 0.466795 0.856208 0.046715 0 \n", + "32558 0.466795 0.856208 0.413569 1 \n", + "32559 0.027346 0.856208 0.106485 0 \n", + "32560 0.322677 0.856208 0.147308 0 \n", + "\n", + "[28214 rows x 14 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scale = MinMaxScaler()\n", + "new_ds.loc[:,'age':'hours.per.week'] = scale.fit_transform(new_ds.loc[:,'age':'hours.per.week'])\n", + "new_ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.092515, + "end_time": "2021-05-22T12:30:25.901974", + "exception": false, + "start_time": "2021-05-22T12:30:25.809459", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "AS we can see from the above table that the data is now more normalised and can be used by the models for learning.\n", + "![](https://kiarofoods.com/wp-content/uploads/2019/10/line_break.png)\n", + "# Data Imbalance:\n", + "If the data is imbalanced, it can cause the overfitting and bias in the odel prediction. So it is important to check and cure the data imbalance if present. We check the target variable to see if it is balanced or not." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:26.103495Z", + "iopub.status.busy": "2021-05-22T12:30:26.102775Z", + "iopub.status.idle": "2021-05-22T12:30:26.213357Z", + "shell.execute_reply": "2021-05-22T12:30:26.213891Z", + "shell.execute_reply.started": "2021-05-22T11:36:51.890668Z" + }, + "papermill": { + "duration": 0.219386, + "end_time": "2021-05-22T12:30:26.214088", + "exception": false, + "start_time": "2021-05-22T12:30:25.994702", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (8, 4))\n", + "plt.title(\"Values distribution in target class: Income\")\n", + "sns.countplot(data = new_ds, x = 'income')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.091496, + "end_time": "2021-05-22T12:30:26.397770", + "exception": false, + "start_time": "2021-05-22T12:30:26.306274", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see that data is imbalanced. In order **to remove the data imbalance, we use the SMOTETomek class to create synthetic values using KNN algorithm.**" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:30:26.586243Z", + "iopub.status.busy": "2021-05-22T12:30:26.585571Z", + "iopub.status.idle": "2021-05-22T12:30:33.837113Z", + "shell.execute_reply": "2021-05-22T12:30:33.837619Z", + "shell.execute_reply.started": "2021-05-22T11:37:49.485554Z" + }, + "papermill": { + "duration": 7.347859, + "end_time": "2021-05-22T12:30:33.837822", + "exception": false, + "start_time": "2021-05-22T12:30:26.489963", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from imblearn.combine import SMOTETomek\n", + "x = new_ds.loc[:,\"age\":\"relationship_enc\"]\n", + "y = new_ds.loc[:,\"income\"]\n", + "smk = SMOTETomek()\n", + "x_new, y_new = smk.fit_resample(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:34.054526Z", + "iopub.status.busy": "2021-05-22T12:30:34.053864Z", + "iopub.status.idle": "2021-05-22T12:30:34.176570Z", + "shell.execute_reply": "2021-05-22T12:30:34.175964Z", + "shell.execute_reply.started": "2021-05-22T11:38:03.630070Z" + }, + "papermill": { + "duration": 0.244575, + "end_time": "2021-05-22T12:30:34.176707", + "exception": false, + "start_time": "2021-05-22T12:30:33.932132", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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HDh1000036YcfftC+ffvUuHFjSdK8efOUl5enQYMGydvb26z/yy+/aOrUqVqxYoUSExOVnZ3ttr9jx46VehtzcnK0c+dOVatWzXxL+lK+vr7at2+fudy/f3999tlnuuOOO9SnTx+1b99ebdq0UXh4eKm16+K3VV1cYwwLG29cErm5uXr33Xe1ePFi7d27V5mZmW7j5oq63i1btixQdvr0aR06dEi1a9cudKhE27ZtC5Rt3rxZFy5cKHK8Z25uriS5XfuS+vHHHyVJXbp0uar9fPnll5o1a5a2bNmi9PT0ArMQpKenm29t9+/fX3/729908803q2/fvoqNjVWbNm1UvXp1t2169OihZ599ViNGjNDq1avVqVMntWnTRjfffPMVTZ11vV4/GzdulCTt3Lmz0MctISFB0m+P28033+y2rrDnTmkr7nW3Uthr0NvbWzVq1HB7De7fv19nzpzRbbfdVuhwprZt2xY6xrgoNptNr7zyiv7+979r9erV+vHHH7Vt2zb99NNPmjNnjubNm6eZM2cWOTZZ+m2IxapVq7R48WL94Q9/0EcffaRu3bopLCysyJkzPMkwjFLZz/79+5WTk6M2bdqoSpUqBdZ36NBBEydOLDBln1T44+36kGxMTEyBoUS1atWSJB09etQsu5p72vr16/X666+rUqVK+v77792GeuHaIDSjTHF9yO+ZZ57RnDlzNG3aNBmGoblz58pms7nd9J1Op26//XYlJSWpZcuWGjBggKpUqSK73S6n06k333yz0A/kXa2MjAwZhqG0tLRif4jvwQcf1PLlyzVt2jS99957evfddyVJLVq00KRJk3TvvfdedbsKmy7KNab6woULV71/SerTp4/i4uJUr149PfDAA6pZs6Z8fX0lSW+88UaR17tmzZoFyk6fPi1JqlGjRqHbFFbu+oDb5s2btXnz5iLbmZWVdfkTKQan0ynpf7/oSuLNN9/Uk08+qcqVK+vee+9VnTp1FBAQIJvNZo4fv/ia/fWvf1W1atU0Y8YMvfXWW3rjjTfMD+BNnTrV/CUdERGhTZs2afz48Vq1apU+++wzSb/9kTRmzJhifeDqer5+XI9bUR/ydSnscSvsuVPainvdrRT1RS92u93tNZiZmSnpyp77xT1+nz591KdPH0lSdna2Jk+erIkTJ2rUqFHq0aNHkfu+//77VaNGDc2ZM0e5ubnKzs6+bMiWfntskpKSlJKSYjmzj+vDcWVt5hXXY1HUmGxXuet+cLHg4OACZa577uXWuYKwdHX3tO3bt+vXX39V69atmVnpOiE0o8wZPHiwxo4dq4ULF2rSpElat26dEhMT1aFDB9WvX9+sN2fOHCUlJWncuHEF/kLfuHGj3nzzzWIdz8vrt0lkiupNcTqdbr8MXTfDZs2aadu2bcU+r27duqlbt27Kzs7WTz/9pOXLl2vmzJnq3r27tm/fXqCHrazZsmWL4uLi1LFjR61cudJtyr/8/Hy9+uqrRW5bWO9nUFCQJOm///1vodsUVu669v/v//0/vf7661fU/ivlesyPHTumpk2bXvH2eXl5Gj9+vGrWrKlt27YV+KXs6n291IABAzRgwAA5nU5t2LBBcXFxeu+999SpUyf9/PPPZu9n48aN9dFHHykvL087d+7UmjVr9Pbbb2v06NGqWLGi/vznP1+2faX1+ikO1+O2c+dO3XrrrVe0bUm+dMI1m0xhCgs/UvGve2koyXO/JCpWrKiXXnpJ3377rdavX68ffvhBDz74YKF1vb29NXjwYE2ePFlHjx5VeHi45bssbdu2VVJSktasWXPZP/xd80VLUps2bUp+QteA67l58uTJQte7puorLASX5vFLck8bOXKkUlNTNWvWLPXo0UNLly6Vv7//tWgm/g9TzqHMqVGjhnr06KH09HQtXbrUfJvy0Ucfdat38OBBSVLv3r0L7OO7774r9vEqV64s6X89IZcew9UT4RIYGKhbbrlF8fHxOnXqVLGP41KxYkV16NBBr7/+up599lmdP39eK1euvOL9XAuutxML65l2Xe8ePXoUmCN706ZNOnPmzBUdKygoSPXq1dOxY8cK/QbC9evXFyhr2bKlvLy8ipxZozCXO6fLadWqlSSV+LFJT0+X0+lU69atCwTmrKwsyz+4HA6HunbtqtmzZ2vQoEE6deqUvv/++wL17Ha7WrRooX/84x/m9FRLly61bF9pvX5cKlSoUOQ1dl3LK3ncrkblypULfT1fuHBBO3bsuOy2xb3uV6NRo0by9/fXrl279OuvvxZYX9hz/2q4hoBYDWkYOnSobDabjh49qiFDhljOVOKa+nPOnDmXDfqvvfaazp07p44dO5a5mTMaNmyogIAA7dy5s9A/qNauXStJRU7Zd7VKck9zsdlsmjlzpp588kl99dVXZqcMrh1CM8ok19uC06ZNU1xcnKpVq6ZevXq51XGNg710vt3t27dr0qRJxT5Wo0aNFBQUpM8//1ypqalm+ZkzZ4p8m/uvf/2rzp8/ryFDhhR6o83IyHALRd9//32hPV+uXzSXTq/nKa6pq44cOVJgXVHXOzU1tcCXIhTXgAEDlJ+fr2eeecbtF3pKSkqh48VDQkLUv39/bdmyRS+99FKhIe3QoUNKSkoyly93TpczcOBABQUFaebMmYWGpovHJRYmJCREAQEB2rp1q9tbq7m5uRo9erTS09MLbLN27dpCg43reel6nmzdurXAH3PSlT2fSuv141K1alWlpaUV+sfT4MGD5XA4NGHCBG3atKnA+vz8/GLPm10cLVu21JEjR/TVV1+5lU+cONGc7vBixb3upcXHx0d9+vRRZmZmganEdu7cqYULF17R/qZOnar4+PhC161fv15r166V3W7XnXfeedn93HTTTVq1apXi4uKKNcTn7rvv1p/+9CedOnVK3bt3L/Q1MWvWLE2ZMkWBgYGl+u5FafHx8VH//v3166+/6oUXXnBbd+jQIb311lvy9vbWn/70p2ty/JLc0y71z3/+U88884zWrl2rTp06mUPfUPoYnoEy6b777lNkZKT5C3bkyJHy8fFxqzNgwABNnTpVTz75pNauXasGDRrowIEDWr58uR588EF99NFHxTqWt7e3Ro8erZdeeknNmjVTr169lJeXp6+//lphYWGFjsEbMmSItm7dqhkzZuimm25Sp06dVKdOHZ06dUpJSUn6/vvvNXjwYM2aNUuS9MQTT+jYsWNq06aNIiMj5ePjo61bt+qbb75RRESE+vbte5VXrHTcc889mjp1qoYNG6bevXurUqVKcjgcGjlypG6//Xa1adNGn332mVq3bq22bdvqv//9r1auXKmGDRuWaKzi3//+dy1dulSLFy/W/v37dd999ykzM1Mff/yx7r77bi1dutQcPuMyffp0HThwQGPHjtX777+vtm3bqkaNGjp+/Lj27dunzZs3a9GiRWaPVsOGDVWrVi0tXrxY3t7eioiIkM1m05/+9CdFREQU2bZq1arp3//+tx566CG1b99eXbp00a233qrTp09r165dSklJuewvMi8vLz3xxBOaPHmymjZtqgceeEDnz5/X2rVrderUKbVv397sxXLp1auXAgMD1apVK0VGRsowDK1bt06bN29WixYtzPly33//fb377rtq27atbrrpJlWuXFmHDh3SF198IV9fX7e5kotSWq8fl3vuuUebN29W586ddffdd8vX11fR0dG6//77VbVqVS1ZskS9evVSq1atdM899+iWW26RzWZTSkqKNm7cqF9++UVnz569omMWZcyYMVq9erUeeOAB9enTR1WqVNGGDRuUlJSkdu3aFQjoxb3upWny5Mn65ptv9Oqrr+qnn35S69atdeLECX388cfq2rVroc/9onz44Yf6+9//rkaNGqlVq1YKDQ1Vdna24uPj9c0338gwDE2bNq1Yr9ErnSP6X//6l/Ly8rRo0SI1bNhQXbp0UYMGDZSdna21a9dqz549qlq1qj799NNSHYJ2uS9+6dmz5xXNVz158mStW7dO06dP1+bNm9W+fXtznuZff/1V06dPv6Y95Fd6TyvMK6+8Ij8/P40bN0733nuvVq1aZb6LilLkqWk7ACuuKcckGT///HOhdeLj443777/fqF69uhEQEGA0b97cmD17tpGUlHTZOX4vneIsPz/fmDRpklGvXj3D29vbqF27tvHUU08Z2dnZl51K64svvjC6detmVK9e3fD29jZq1Khh3H777cZzzz3nNg3TRx99ZPTt29eoX7++UbFiRaNSpUrGLbfcYjz77LNGampqsa6H1ZRzhXFN+VbYvNdFmTZtmtGoUSNzrtiLz/2XX34xHn/8cSMiIsLw9fU16tWrZzzzzDNFXqfiHD8jI8MYNWqUERoaavj4+BgNGzY0XnvtNeOnn34yJBmjR48usM25c+eMt99+27jzzjvNeVRr165tdOjQwfjnP/9ZYI7aTZs2GR06dDCCgoIMm81mSDLWrl1brOuxZ88e409/+pMRFhZmeHt7GyEhIcbdd99dYLo3FTJdVm5urjFt2jSjcePGhp+fn1GjRg3jkUceMZKTkwt9Ls6cOdPo2bOnUbduXcPf39+oXLmyERMTY0yZMsVtarIff/zReOyxx4xbb73VqFy5suHn52fcdNNNxqBBg4zdu3cX67wM48pfP5ebci4rK8t47LHHjFq1ahkVKlQodPukpCRjxIgRRv369Q1fX1+jUqVKRsOGDY1HHnnEiIuLc6tb0ukPXT7//HOjRYsWhq+vr1GlShWjT58+V33dDePyU85dybSVhmEYR48eNQYMGGBUq1bN8PPzM6Kjo4358+cbn3zyiSHJ+Oc//1msc922bZvx0ksvGe3btzciIyMNPz8/8/XZr18/Y926dQW2uXTKucspasq5i61evdp46KGHjFq1ahk+Pj5GUFCQ0bx5c2PcuHGFTuV2qdKacu5y95vLHSMjI8P4+9//btSvX9/w8fExgoODjY4dOxqrV68ucj+FPd5FvXYubn9hx7+Se9rljv/qq68akoxmzZoV+zsAUHw2wyileVsAoBTNnj1bjz76qGbNmlXot3EBN6rnnntOr7zyilatWqVOnTp5ujkA/g+hGYBHHT9+vMDbxkeOHFHbtm114sQJHT58uMxNUwWUhsKe+7t371br1q3l4+OjY8eOyc/Pz0OtA3ApxjQD8KjevXsrNzdXLVq0kMPhUHJyspYvX66cnBxNmjSJwIwb1m233ab69eurSZMmqlixog4cOKAvv/xS+fn5evfddwnMQBlDTzMAj5oxY4bef/99HThwQJmZmQoMDFSzZs00cuTIIueUBW4EEyZM0NKlS5WcnKxff/1VDodDrVq10pgxY9SuXTtPNw/AJQjNAAAAgAXmaQYAAAAsEJoBAAAAC4RmAAAAwAKhGQAAALDAlHPXWEZGhvLy8jzdDAAAAFzCbrcX+yvHCc3XWF5ennJzcz3dDAAAAFwFhmcAAAAAFgjNAAAAgAVCMwAAAGCB0AwAAABYIDQDAAAAFgjNAAAAgAVCMwAAAGCB0AwAAABYIDQDAAAAFgjNAAAAgAVCMwAAAGDB7ukG4No48dRQTzcBwDUSOnWOp5vgEYMWbPR0EwBcI/MH3unpJliipxkAAACwQGgGAAAALBCaAQAAAAuEZgAAAMACoRkAAACwQGgGAAAALBCaAQAAAAuEZgAAAMACoRkAAACwQGgGAAAALBCaAQAAAAt2TzfgYnFxcdq0aZOOHTsmHx8fRUVF6ZFHHlFYWJhZ5/z581q4cKE2bNig3NxcRUdHa+jQoXI4HGad9PR0zZ49W/Hx8fLz81NsbKz69eunChUqmHXi4+O1cOFCpaSkqGrVqurdu7fatWvn1p5Vq1bpiy++kNPpVEREhIYMGaL69etf68sAAACAMqZM9TTv3btXnTp10ssvv6znn39eFy5c0MSJE3X27FmzzoIFC7R161b99a9/1YQJE5SRkaFp06aZ6/Pz8zVp0iTl5eVp4sSJGjFihL799lt99NFHZp3U1FRNnjxZt9xyi1599VV169ZNs2bN0o4dO8w6GzZs0MKFC/XQQw9pypQpioiI0Msvv6zMzMzrci0AAABQdpSp0Pzcc8+pXbt2ql27tiIjIzVixAilp6crMTFRkpSTk6NvvvlGAwcOVJMmTVSvXj0NHz5c+/fvV0JCgiRp586dOnr0qEaNGqXIyEg1a9ZMffr00erVq5WXlydJ+uqrrxQSEqIBAwYoPDxcnTt3VqtWrfTll1+abVm+fLnuuecetW/fXuHh4Ro2bJh8fHy0du3a639hAAAA4FFlKjRfKicnR5IUGBgoSUpMTNSFCxfUtGlTs06tWrVUrVo1MzQnJCSoTp06bsM1YmJidObMGaWkpEiSDhw44LYPSYqOjjb3kZeXp8TERLc6Xl5eatq0qVnnUrm5ucrJyTH/nTlzxlxns9mu+z8ANy5P3FPKwj8AN67ycF8pU2OaL5afn6/58+erYcOGqlOnjiTJ6XTKbrerYsWKbnWDg4PldDrNOhcHZtd61zrX/66yi+ucOXNG58+fV1ZWlvLz8wvsx+Fw6Pjx44W2Ny4uTkuWLDGX69atqylTpqh69epXctqlpvBWArgRhIaGeroJAFCqysN9rcyG5rlz5yolJUUvvviip5tSLL169VL37t3NZddfL2lpaeawEAAoDSdOnPB0EwCgVHnqvma324vdwVkmQ/PcuXO1bds2TZgwQVWrVjXLHQ6H8vLylJ2d7dbbnJmZafYKOxwOHTx40G1/rg/vXVzn0g/0ZWZmyt/fXz4+PgoKCpKXl5fZM+1SWC+2i7e3t7y9vQtdZxiG1SkDQLFxTwFwoykP97UyNabZMAzNnTtXmzZt0tixYxUSEuK2vl69eqpQoYJ2795tlh0/flzp6emKioqSJEVFRenIkSNuoXjXrl3y9/dXeHi4JKlBgwZu+3DVce3DbrerXr162rNnj7k+Pz9fe/bsMesAAADg96NMhea5c+dq3bp1Gj16tPz9/eV0OuV0OnX+/HlJUkBAgDp06KCFCxdqz549SkxM1IwZMxQVFWWG2ejoaIWHh2v69OlKTk7Wjh07tHjxYnXq1MnsCb7vvvuUmpqqDz74QMeOHdPq1au1ceNGdevWzWxL9+7d9Z///Efffvutjh49qjlz5ujcuXMF5nIGAADAjc9mlKH+8IcffrjQ8uHDh5th1fXlJj/88IPy8vIK/XKTtLQ0zZkzR/Hx8fL19VVsbKz69+9f4MtNFixYoKNHj172y02WLVsmp9OpyMhIDR48WA0aNLiic0pLS1Nubu4VbVMaTjw19LofE8D1ETp1jqeb4BGDFmz0dBMAXCPzB97pkeN6e3sXe0xzmQrNNyJCM4DSRmgGcKMpD6G5TA3PAAAAAMoiQjMAAABggdAMAAAAWCA0AwAAABYIzQAAAIAFQjMAAABggdAMAAAAWCA0AwAAABYIzQAAAIAFQjMAAABggdAMAAAAWCA0AwAAABYIzQAAAIAFQjMAAABggdAMAAAAWCA0AwAAABYIzQAAAIAFQjMAAABggdAMAAAAWCA0AwAAABYIzQAAAIAFQjMAAABggdAMAAAAWCA0AwAAABbsnm7Axfbu3atly5YpKSlJGRkZGjNmjFq2bGmuf/jhhwvd7pFHHlGPHj0kSSNGjFBaWprb+n79+qlnz57m8uHDhzV37lwdOnRIQUFB6ty5sx544AG3bTZu3KiPPvpIaWlpqlmzpvr376/mzZuX0pkCAACgPClTofncuXOKjIxUhw4d9NprrxVY/69//cttefv27Zo1a5buuOMOt/KHH35YHTt2NJf9/PzMn3NycjRx4kQ1bdpUw4YN05EjRzRz5kxVrFjR3Gb//v1688031a9fPzVv3lzr16/X1KlTNWXKFNWpU6c0TxkAAADlQJkKzc2aNVOzZs2KXO9wONyWN2/erFtuuUU1atRwK/f39y9Q12X9+vXKy8vT8OHDZbfbVbt2bSUnJ2v58uVmaF6xYoViYmLM3uu+fftq9+7dWrVqlR599NGSnyAAAADKpTIVmq+E0+nU9u3bNWLEiALrli5dqk8//VTVqlVT27Zt1a1bN1WoUEGSlJCQoMaNG8tu/9+pR0dH6/PPP1dWVpYCAwOVkJCg7t27u+0zOjpamzdvLrI9ubm5ys3NNZdtNpv8/f3NnwGgtHBPAXCjKQ/3tXIbmr/77jv5+fm5jXmWpC5duqhu3boKDAzU/v37tWjRImVkZGjgwIGSfgvbISEhbtu4eqWdTqcCAwPldDoVHBzsVic4OFhOp7PI9sTFxWnJkiXmct26dTVlyhRVr179Ks6y5I575KgArofQ0FBPNwEASlV5uK+V29C8du1a3XXXXfLx8XErv7iHOCIiQna7XbNnz1a/fv3k7e19zdrTq1cvt2O7/mJKS0tTXl7eNTsugN+fEydOeLoJAFCqPHVfs9vtxe7gLJehed++fTp+/LiefPJJy7oNGjTQhQsXlJaWprCwMDkcjgI9xq5lV4+zw+FQZmamW53MzMwix0lLkre3d5Gh3DAMy3YCQHFxTwFwoykP97VyOU/zN998o3r16ikyMtKybnJysmw2m4KCgiRJUVFR2rdvn1vv765duxQWFqbAwECzzu7du932s2vXLjVo0KD0TgIAAADlRpkKzWfPnlVycrKSk5MlSampqUpOTlZ6erpZJycnRz/++KM6dOhQYPuEhAR9+eWXSk5O1n//+1+tW7dOCxYs0F133WUG4rZt28put2vWrFlKSUnRhg0btHLlSrehFV27dtXOnTv1xRdf6NixY/r444916NAhde7c+dpeAAAAAJRJZWp4xqFDhzRhwgRzeeHChZKk2NhYc5aMDRs2yDAMtW3btsD2drtdGzZs0CeffKLc3FyFhISoW7duboE4ICBAzz//vObOnaunn35alSpVUu/evd3mdW7YsKGeeOIJLV68WIsWLVJoaKieeuop5mgGAAD4nbIZ5WEQSTmWlpbmNhXd9XLiqaHX/ZgAro/QqXM83QSPGLRgo6ebAOAamT/wTo8c19vbu9gfBCxTwzMAAACAsojQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABbunG3CxvXv3atmyZUpKSlJGRobGjBmjli1bmuvfeecdfffdd27bREdH67nnnjOXs7Ky9N5772nr1q2y2Wy64447NHjwYPn5+Zl1Dh8+rLlz5+rQoUMKCgpS586d9cADD7jtd+PGjfroo4+UlpammjVrqn///mrevPk1OnMAAACUZWUqNJ87d06RkZHq0KGDXnvttULrxMTEaPjw4eay3e5+Cm+99ZYyMjL0/PPP68KFC5oxY4beffddjR49WpKUk5OjiRMnqmnTpho2bJiOHDmimTNnqmLFiurYsaMkaf/+/XrzzTfVr18/NW/eXOvXr9fUqVM1ZcoU1alT5xqdPQAAAMqqMjU8o1mzZurbt69b7/Kl7Ha7HA6H+S8wMNBcd/ToUe3YsUOPPfaYGjRooEaNGmnIkCHasGGDTp06JUlav3698vLyNHz4cNWuXVtt2rRRly5dtHz5cnM/K1asUExMjHr06KHw8HD17dtX9erV06pVq67dyQMAAKDMKlM9zcWxd+9eDR06VBUrVlSTJk3Ut29fVapUSZKUkJCgihUr6qabbjLrN23aVDabTQcPHlTLli2VkJCgxo0bu/VQR0dH6/PPP1dWVpYCAwOVkJCg7t27ux03OjpamzdvLrJdubm5ys3NNZdtNpv8/f3NnwGgtHBPAXCjKQ/3tXIVmmNiYnTHHXcoJCREJ0+e1KJFi/TKK6/o5ZdflpeXl5xOp4KCgty2qVChggIDA+V0OiVJTqdTISEhbnUcDoe5zlU3ODjYrU5wcLC5j8LExcVpyZIl5nLdunU1ZcoUVa9eveQnfBWOe+SoAK6H0NBQTzcBAEpVebivlavQ3KZNG/PnOnXqKCIiQqNGjVJ8fLyaNm3qwZZJvXr1cuuddv3FlJaWpry8PE81C8AN6MSJE55uAgCUKk/d1+x2e7E7OMtVaL5UjRo1VKlSJZ08eVJNmzaVw+HQ6dOn3epcuHBBWVlZZm+yw+Eo0GPsWr64TmZmpludzMxMc31hvL295e3tXeg6wzCKfU4AYIV7CoAbTXm4r5WpDwJeqV9++UVZWVmqXLmyJCkqKkrZ2dlKTEw06+zZs0eGYah+/fpmnX379rn1/u7atUthYWHmhwqjoqK0e/dut2Pt2rVLDRo0uNanBAAAgDKoTIXms2fPKjk5WcnJyZKk1NRUJScnKz09XWfPntX777+vhIQEpaamavfu3Xr11VdVs2ZNRUdHS5LCw8MVExOjd999VwcPHtTPP/+s9957T61bt1aVKlUkSW3btpXdbtesWbOUkpKiDRs2aOXKlW5DK7p27aqdO3fqiy++0LFjx/Txxx/r0KFD6ty583W/JgAAAPA8m1GG+sPj4+M1YcKEAuWxsbEaNmyYpk6dqqSkJGVnZ6tKlSq69dZb1adPH7dhE1lZWZo7d67bl5sMGTKkyC83qVSpkjp37qyePXu6HXPjxo1avHix0tLSFBoaWuIvN0lLS3ObVeN6OfHU0Ot+TADXR+jUOZ5ugkcMWrDR000AcI3MH3inR47r7e1d7DHNZSo034gIzQBKG6EZwI2mPITmMjU8AwAAACiLCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYMHu6QZcbO/evVq2bJmSkpKUkZGhMWPGqGXLlpKkvLw8LV68WNu3b1dqaqoCAgLUtGlT9evXT1WqVDH3MWLECKWlpbntt1+/furZs6e5fPjwYc2dO1eHDh1SUFCQOnfurAceeMBtm40bN+qjjz5SWlqaatasqf79+6t58+bX7uQBAABQZpWp0Hzu3DlFRkaqQ4cOeu2119zWnT9/XklJSerdu7ciIyOVlZWl+fPn69VXX9XkyZPd6j788MPq2LGjuezn52f+nJOTo4kTJ6pp06YaNmyYjhw5opkzZ6pixYrmNvv379ebb76pfv36qXnz5lq/fr2mTp2qKVOmqE6dOtfwCgAAAKAsKlOhuVmzZmrWrFmh6wICAvTCCy+4lQ0ZMkTPPvus0tPTVa1aNbPc399fDoej0P2sX79eeXl5Gj58uOx2u2rXrq3k5GQtX77cDM0rVqxQTEyMevToIUnq27evdu/erVWrVunRRx8thTMFAABAeVLi0Pzdd9+pcePGCgkJKXR9amqq9u3bp9jY2BI3zkpOTo5sNpsCAgLcypcuXapPP/1U1apVU9u2bdWtWzdVqFBBkpSQkKDGjRvLbv/fqUdHR+vzzz9XVlaWAgMDlZCQoO7du7vtMzo6Wps3by6yLbm5ucrNzTWXbTab/P39zZ8BoLRwTwFwoykP97USh+YZM2Zo1KhRRYbmgwcPasaMGdcsNJ8/f14ffvih2rRp4xaau3Tporp16yowMFD79+/XokWLlJGRoYEDB0qSnE5ngTa7eqWdTqcCAwPldDoVHBzsVic4OFhOp7PI9sTFxWnJkiXmct26dTVlyhRVr179Ks+0ZI575KgArofQ0FBPNwEASlV5uK9ds+EZZ8+eNXt3S1teXp7++c9/SpKGDh3qtu7iHuKIiAjZ7XbNnj1b/fr1k7e39zVpjyT16tXL7diuv5jS0tKUl5d3zY4L4PfnxIkTnm4CAJQqT93X7HZ7sTs4ryg0Hz58WMnJyebyvn37dOHChQL1srOz9fXXX1+TvxpcgTk9PV1jx44tMDTjUg0aNNCFCxeUlpamsLAwORyOAj3GrmVXj7PD4VBmZqZbnczMzCLHSUuSt7d3kaHcMIzLthEArgT3FAA3mvJwX7ui0Lxp0ya3IQhr1qzRmjVrCq0bEBCgkSNHXl3rLuEKzCdPntS4ceNUqVIly22Sk5Nls9kUFBQkSYqKitKiRYuUl5dnjmvetWuXwsLCFBgYaNbZvXu3unXrZu5n165datCgQameDwAAAMqHKwrNHTt2VIsWLWQYhp599lk9/PDDhc524efnpxo1alzx8IyzZ8/q5MmT5nJqaqqSk5MVGBgoh8Oh119/XUlJSfrHP/6h/Px8s4c4MDBQdrtdCQkJOnDggG655Rb5+/srISFBCxYs0F133WUG4rZt2+qTTz7RrFmz9MADDyglJUUrV640xzxLUteuXTV+/Hh98cUXat68uX744QcdOnSImTMAAAB+p2xGCfvD9+7dq1q1ahX4wNzViI+P14QJEwqUx8bG6g9/+EORPdfjxo3TLbfcosTERM2dO1fHjh1Tbm6uQkJCdPfdd6t79+5uQycu/nKTSpUqqXPnzm5ffiL99uUmixcvVlpamkJDQ0v85SZpaWlus2pcLyeeGmpdCUC5FDp1jqeb4BGDFmz0dBMAXCPzB97pkeN6e3sXe0xziUMziofQDKC0EZoB3GjKQ2i+qtkzduzYoW+++UapqanKzs4uMIjbZrPp7bffvppDAAAAAB5X4tC8bNkyffjhh3I4HLrpppv4emkAAADcsEocmlesWKEmTZromWeecft2PQAAAOBG41XSDbOzs9WqVSsCMwAAAG54JQ7N9evX1/HjfFkzAAAAbnwlDs1//vOftWnTJq1fv7402wMAAACUOSUeW/HGG2/owoULevvttzV79mxVrVpVXl7uGdxms2nq1KlX3UgAAADAk0ocmgMDA1WpUiWFhoaWZnsAAACAMqfEoXn8+PGl2AwAAACg7CrxmGYAAADg96LEPc179+4tVr2bb765pIcAAAAAyoQSh+YJEyYUq95HH31U0kMAAAAAZUKJQ/O4ceMKlOXn5ys1NVX/+c9/lJ+fr/79+19V4wAAAICyoMSh+XLDLtq1a6dx48YpPj5eTZo0KekhAAAAgDLhmnwQ0MvLS61bt9Y333xzLXYPAAAAXFfXbPaMrKwsZWdnX6vdAwAAANdNiYdnpKenF1qenZ2tffv2admyZWrcuHGJGwYAAACUFSUOzSNGjLjs+gYNGmjYsGEl3T0AAABQZpQ4ND/++OMFymw2mypWrKiaNWsqPDz8qhoGAAAAlBUlDs3t2rUrxWYAAAAAZVeJQ/PFjh49qrS0NElS9erV6WUGAADADeWqQvPmzZu1cOFCpaamupWHhIRo4MCBuu22266qcQAAAEBZUOLQvG3bNk2bNk3Vq1fXH//4R7N3+ejRo/rPf/6j1157TU8//bRiYmJKq60AAACAR5Q4NH/66aeKiIjQhAkT5OfnZ5bfdttt6ty5s8aOHatPPvmE0AwAAIByr8Sh+ciRI/rjH//oFphd/Pz81K5dOy1atOiK9rl3714tW7ZMSUlJysjI0JgxY9SyZUtzvWEY+vjjj/Wf//xH2dnZatSokYYOHarQ0FCzTlZWlt577z1t3bpVNptNd9xxhwYPHuzWzsOHD2vu3Lk6dOiQgoKC1LlzZz3wwANubdm4caM++ugjpaWlqWbNmurfv7+aN29+RecDAACAG0OJvxHQ29tbWVlZRa7PysqSt7f3Fe3z3LlzioyM1J///OdC13/++edauXKlhg0bpldeeUW+vr56+eWXdf78ebPOW2+9pZSUFD3//PN6+umntW/fPr377rvm+pycHE2cOFHVqlXT5MmT9cgjj+iTTz7RmjVrzDr79+/Xm2++qQ4dOmjKlCm6/fbbNXXqVB05cuSKzgcAAAA3hhKH5iZNmmjFihVKSEgosO7AgQNauXKlmjZtekX7bNasmfr27evWu+xiGIZWrFihBx98ULfffrsiIiI0cuRIZWRkaPPmzZJ+G0+9Y8cOPfbYY2rQoIEaNWqkIUOGaMOGDTp16pQkaf369crLy9Pw4cNVu3ZttWnTRl26dNHy5cvNY61YsUIxMTHq0aOHwsPD1bdvX9WrV0+rVq26ovMBAADAjaHEwzMeeeQRPffcc3rhhRdUv359hYWFSZKOHz+ugwcPKjg4WP379y+1hqampsrpdOrWW281ywICAlS/fn0lJCSoTZs2SkhIUMWKFXXTTTeZdZo2bSqbzaaDBw+qZcuWSkhIUOPGjWW3/+/Uo6Oj9fnnnysrK0uBgYFKSEhQ9+7d3Y4fHR1thvPC5ObmKjc311y22Wzy9/c3fwaA0sI9BcCNpjzc10ocmkNCQvTaa68pLi5OO3bs0IYNGyT9Nk9z165d1bNnTwUHB5daQ51OpyQV2GdwcLC5zul0KigoyG19hQoVFBgY6FYnJCTErY7D4TDXuepe7jiFiYuL05IlS8zlunXrasqUKapevXoxz7B0HffIUQFcDxd/jgMAbgTl4b5W4tB84cIFeXt7a9CgQYWuz8nJ0YULF1ShQoWSHqJc6dWrl1vvtOsvprS0NOXl5XmqWQBuQCdOnPB0EwCgVHnqvma324vdwVni0Dxv3jzt27dP06ZNK3T9Cy+8oCZNmmjw4MElPYQbV29wZmamKleubJZnZmYqMjLSrHP69Gm37S5cuKCsrCxze4fDUaDH2LV8cZ3MzEy3OpmZmeb6wnh7exf5wUfDMIo+MQC4QtxTANxoysN9rcQfBNyxY4fuuOOOIte3atVK27dvL+nuCwgJCZHD4dDu3bvNspycHB08eFBRUVGSpKioKGVnZysxMdGss2fPHhmGofr165t19u3b59b7u2vXLoWFhSkwMNCsc/FxXHUaNGhQaucDAACA8qPEoTkjI0NVqlQpcn3lypXNGSuK6+zZs0pOTlZycrKk3z78l5ycrPT0dNlsNnXt2lWfffaZtmzZoiNHjmj69OmqXLmybr/9dklSeHi4YmJi9O677+rgwYP6+eef9d5776l169ZmW9u2bSu73a5Zs2YpJSVFGzZs0MqVK92GVnTt2lU7d+7UF198oWPHjunjjz/WoUOH1Llz5yu8SgAAALgRlHh4RmBgoI4fL/rjZseOHTNnjyiuQ4cOacKECebywoULJUmxsbEaMWKEHnjgAZ07d07vvvuucnJy1KhRIz377LPy8fExt3niiSc0d+5cvfjii+aXmwwZMsRcHxAQoOeff15z587V008/rUqVKql3797q2LGjWadhw4Z64okntHjxYi1atEihoaF66qmnVKdOnSs6HwAAANwYbEYJB5HMnDlTGzdu1IQJE1S3bl23dYmJiRo3bpzuvPNODR8+vFQaWl6lpaW5TUV3vZx4auh1PyaA6yN06hxPN8EjBi3Y6OkmALhG5g+80yPH9fb2vvYfBOzTp4927NihZ599Vi1atFDt2rUlSSkpKdq6dauCgoLUp0+fku4eAAAAKDNKHJqrVKmiyZMn68MPP9SWLVvML/7w9/dX27Zt9cc//vGyY54BAACA8qLEoVn67cN+I0eOlGEY5lRvQUFB5eJbXQAAAIDiuqrQ7GKz2Ur12/8AAACAsqTEU84BAAAAvxeEZgAAAMACoRkAAACwQGgGAAAALBCaAQAAAAuEZgAAAMACoRkAAACwQGgGAAAALBCaAQAAAAuEZgAAAMACoRkAAACwQGgGAAAALBCaAQAAAAuEZgAAAMACoRkAAACwQGgGAAAALBCaAQAAAAuEZgAAAMACoRkAAACwQGgGAAAALNg93YArNWLECKWlpRUov++++zR06FCNHz9ee/fudVvXsWNHPfroo+Zyenq6Zs+erfj4ePn5+Sk2Nlb9+vVThQoVzDrx8fFauHChUlJSVLVqVfXu3Vvt2rW7ZucFAACAsqvcheZJkyYpPz/fXD5y5IgmTpyoO++80yy755571KdPH3PZx8fH/Dk/P1+TJk2Sw+HQxIkTlZGRoenTp6tChQrq16+fJCk1NVWTJ0/Wvffeq1GjRmnPnj2aNWuWHA6HYmJirv1JAgAAoEwpd6E5KCjIbXnp0qWqUaOGbr75ZrPM19dXDoej0O137typo0eP6oUXXpDD4VBkZKT69OmjDz/8UA8//LDsdru++uorhYSEaMCAAZKk8PBw/fzzz/ryyy8JzQAAAL9D5S40XywvL0/r1q1Tt27dZLPZzPJ169Zp3bp1cjgcatGihXr37i1fX19JUkJCgurUqeMWqmNiYjRnzhylpKSobt26OnDggJo2bep2rOjoaM2fP7/ItuTm5io3N9dcttls8vf3N38GgNLCPQXAjaY83NfKdWjetGmTsrOz3cYat23bVtWqVVOVKlV0+PBhffjhhzp+/LjGjBkjSXI6nQV6oYODg811rv9dZRfXOXPmjM6fP+823MMlLi5OS5YsMZfr1q2rKVOmqHr16qVwplfuuEeOCuB6CA0N9XQTAKBUlYf7WrkOzWvXrlVMTIyqVKlilnXs2NH8uU6dOqpcubJefPFFnTx5UjVr1rxmbenVq5e6d+9uLrv+YkpLS1NeXt41Oy6A358TJ054ugkAUKo8dV+z2+3F7uAst6E5LS1Nu3btMnuQi1K/fn1JMkOzw+HQwYMH3epkZmZKktkD7XA4zLKL6/j7+xfayyxJ3t7e8vb2LnSdYRiW5wMAxcU9BcCNpjzc18rtPM1r165VcHCwmjdvftl6ycnJkqTKlStLkqKionTkyBG3ULxr1y75+/srPDxcktSgQQPt3r3bbT+7du1SVFRUKZ4BAAAAyotyGZrz8/P17bffKjY21m1u5ZMnT2rJkiVKTExUamqqtmzZonfeeUeNGzdWRESEpN8+0BceHq7p06crOTlZO3bs0OLFi9WpUyezp/i+++5TamqqPvjgAx07dkyrV6/Wxo0b1a1bN4+cLwAAADyrXA7P2L17t9LT09W+fXu3crvdrt27d2vFihU6d+6cqlatqjvuuEMPPvigWcfLy0tPP/205syZo+eff16+vr6KjY11m9c5JCRETz/9tBYsWKAVK1aoatWqeuyxx5huDgAA4HfKZpSHQSTlWFpamttUdNfLiaeGXvdjArg+QqfO8XQTPGLQgo2ebgKAa2T+wDutK10D3t7exf4gYLkcngEAAABcT4RmAAAAwAKhGQAAALBAaAYAAAAsEJoBAAAAC4RmAAAAwAKhGQAAALBAaAYAAAAsEJoBAAAAC4RmAAAAwAKhGQAAALBAaAYAAAAsEJoBAAAAC4RmAAAAwAKhGQAAALBAaAYAAAAsEJoBAAAAC4RmAAAAwAKhGQAAALBAaAYAAAAsEJoBAAAAC4RmAAAAwAKhGQAAALBg93QDrsTHH3+sJUuWuJWFhYXpjTfekCSdP39eCxcu1IYNG5Sbm6vo6GgNHTpUDofDrJ+enq7Zs2crPj5efn5+io2NVb9+/VShQgWzTnx8vBYuXKiUlBRVrVpVvXv3Vrt27a7DGQIAAKAsKlehWZJq166tF154wVz28vpfZ/mCBQu0bds2/fWvf1VAQIDmzp2radOm6aWXXpIk5efna9KkSXI4HJo4caIyMjI0ffp0VahQQf369ZMkpaamavLkybr33ns1atQo7dmzR7NmzZLD4VBMTMx1PVcAAACUDeVueIaXl5ccDof5LygoSJKUk5Ojb775RgMHDlSTJk1Ur149DR8+XPv371dCQoIkaefOnTp69KhGjRqlyMhINWvWTH369NHq1auVl5cnSfrqq68UEhKiAQMGKDw8XJ07d1arVq305ZdfeuycAQAA4Fnlrqf55MmT+stf/iJvb29FRUWpX79+qlatmhITE3XhwgU1bdrUrFurVi1Vq1ZNCQkJioqKUkJCgurUqeM2XCMmJkZz5sxRSkqK6tatqwMHDrjtQ5Kio6M1f/78y7YrNzdXubm55rLNZpO/v7/5MwCUFu4pAG405eG+Vq5Cc4MGDTR8+HCFhYUpIyNDS5Ys0dixYzVt2jQ5nU7Z7XZVrFjRbZvg4GA5nU5JktPpdAvMrvWuda7/XWUX1zlz5ozOnz8vHx+fQtsWFxfnNt66bt26mjJliqpXr34VZ1xyxz1yVADXQ2hoqKebAAClqjzc18pVaG7WrJn5c0REhBmiN27cWGSYvV569eql7t27m8uuv5jS0tLMoR8AUBpOnDjh6SYAQKny1H3NbrcXu4OzXIXmS1WsWFFhYWE6efKkbr31VuXl5Sk7O9uttzkzM9PsXXY4HDp48KDbPjIzM811rv9dZRfX8ff3v2ww9/b2lre3d6HrDMO40lMDgCJxTwFwoykP97Vy90HAi509e1YnT56Uw+FQvXr1VKFCBe3evdtcf/z4caWnpysqKkqSFBUVpSNHjriF4l27dsnf31/h4eGSfhsCcvE+XHVc+wAAAMDvT7kKzQsXLtTevXuVmpqq/fv3a+rUqfLy8lLbtm0VEBCgDh06aOHChdqzZ48SExM1Y8YMRUVFmYE3Ojpa4eHhmj59upKTk7Vjxw4tXrxYnTp1MnuJ77vvPqWmpuqDDz7QsWPHtHr1am3cuFHdunXz5KkDAADAg8rV8IxTp07pzTff1K+//qqgoCA1atRIL7/8sjnt3MCBA2Wz2TRt2jTl5eWZX27i4uXlpaefflpz5szR888/L19fX8XGxqpPnz5mnZCQED399NNasGCBVqxYoapVq+qxxx5jjmYAAIDfMZtRHgaRlGNpaWluU9FdLyeeGmpdCUC5FDp1jqeb4BGDFmz0dBMAXCPzB97pkeN6e3sX+4OA5Wp4BgAAAOAJhGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAAADAAqEZAAAAsEBoBgAAACzYPd2AKxEXF6dNmzbp2LFj8vHxUVRUlB555BGFhYWZdcaPH6+9e/e6bdexY0c9+uij5nJ6erpmz56t+Ph4+fn5KTY2Vv369VOFChXMOvHx8Vq4cKFSUlJUtWpV9e7dW+3atbvm5wgAAICyp1yF5r1796pTp0666aabdOHCBS1atEgTJ07U66+/Lj8/P7PePffcoz59+pjLPj4+5s/5+fmaNGmSHA6HJk6cqIyMDE2fPl0VKlRQv379JEmpqamaPHmy7r33Xo0aNUp79uzRrFmz5HA4FBMTc93OFwAAAGVDuRqe8dxzz6ldu3aqXbu2IiMjNWLECKWnpysxMdGtnq+vrxwOh/kvICDAXLdz504dPXpUo0aNUmRkpJo1a6Y+ffpo9erVysvLkyR99dVXCgkJ0YABAxQeHq7OnTurVatW+vLLL6/r+QIAAKBsKFc9zZfKycmRJAUGBrqVr1u3TuvWrZPD4VCLFi3Uu3dv+fr6SpISEhJUp04dORwOs35MTIzmzJmjlJQU1a1bVwcOHFDTpk3d9hkdHa358+cX2Zbc3Fzl5uaayzabTf7+/ubPAFBauKcAuNGUh/tauQ3N+fn5mj9/vho2bKg6deqY5W3btlW1atVUpUoVHT58WB9++KGOHz+uMWPGSJKcTqdbYJak4OBgc53rf1fZxXXOnDmj8+fPuw33cImLi9OSJUvM5bp162rKlCmqXr16aZzuFTvukaMCuB5CQ0M93QQAKFXl4b5WbkPz3LlzlZKSohdffNGtvGPHjubPderUUeXKlfXiiy/q5MmTqlmz5jVrT69evdS9e3dz2fUXU1pamjnsAwBKw4kTJzzdBAAoVZ66r9nt9mJ3cJbL0Dx37lxt27ZNEyZMUNWqVS9bt379+pJkhmaHw6GDBw+61cnMzJQkswfa4XCYZRfX8ff3L7SXWZK8vb3l7e1d6DrDMCzPCQCKi3sKgBtNebivlasPAhqGoblz52rTpk0aO3asQkJCLLdJTk6WJFWuXFmSFBUVpSNHjriF4l27dsnf31/h4eGSpAYNGmj37t1u+9m1a5eioqJK6UwAAABQnpSr0Dx37lytW7dOo0ePlr+/v5xOp5xOp86fPy/pt97kJUuWKDExUampqdqyZYveeecdNW7cWBEREZJ++0BfeHi4pk+fruTkZO3YsUOLFy9Wp06dzJ7i++67T6mpqfrggw907NgxrV69Whs3blS3bt08du4AAADwHJtRHvrD/8/DDz9caPnw4cPVrl07paen6+2331ZKSorOnTunqlWrqmXLlnrwwQfdpp1LS0vTnDlzFB8fL19fX8XGxqp///4FvtxkwYIFOnr06FV9uUlaWprbrBrXy4mnhl73YwK4PkKnzvF0Ezxi0IKNnm4CgGtk/sA7PXJcb2/vYo9pLlehuTwiNAMobYRmADea8hCay9XwDAAAAMATCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABUIzAAAAYIHQDAAAAFiwe7oBZd2qVav0xRdfyOl0KiIiQkOGDFH9+vU93SwAAABcR/Q0X8aGDRu0cOFCPfTQQ5oyZYoiIiL08ssvKzMz09NNAwAAwHVEaL6M5cuX65577lH79u0VHh6uYcOGycfHR2vXrvV00wAAAHAdMTyjCHl5eUpMTFTPnj3NMi8vLzVt2lQJCQkF6ufm5io3N9dcttls8vf3l93umUvsH3mTR44L4Nrz9vb2dBM8on4Nh6ebAOAa8dR97UpyGqG5CKdPn1Z+fr4cDodbucPh0PHjxwvUj4uL05IlS8zlNm3aaPTo0apcufK1bmqhqr/8tkeOCwDXyowh93i6CQB+xwjNpaRXr17q3r27W1lubu7vtkcI18+ZM2c0fvx4jR8/Xv7+/p5uDgBcNe5rKIsIzUUICgqSl5eXnE6nW7nT6SzQ+yz99rYCARmeYBiGkpKSZBiGp5sCAKWC+xrKIj4IWAS73a569eppz549Zll+fr727NmjqKgoD7YMAAAA1xs9zZfRvXt3vfPOO6pXr57q16+vFStW6Ny5c2rXrp2nmwYAAIDriNB8Ga1bt9bp06f18ccfy+l0KjIyUs8++2yhwzMAT/H29tZDDz3E8CAANwzuayiLbAYDhgAAAIDLYkwzAAAAYIHQDAAAAFggNAMAAAAWCM0AAACABWbPAMq5VatW6YsvvpDT6VRERISGDBmi+vXre7pZAHDF9u7dq2XLlikpKUkZGRkaM2aMWrZs6elmAZLoaQbKtQ0bNmjhwoV66KGHNGXKFEVEROjll19WZmamp5sGAFfs3LlzioyM1J///GdPNwUogNAMlGPLly/XPffco/bt2ys8PFzDhg2Tj4+P1q5d6+mmAcAVa9asmfr27UvvMsokQjNQTuXl5SkxMVFNmzY1y7y8vNS0aVMlJCR4sGUAANx4CM1AOXX69Gnl5+cX+IZKh8Mhp9PpkTYBAHCjIjQDAAAAFgjNQDkVFBQkLy+vAr3KTqezQO8zAAC4OoRmoJyy2+2qV6+e9uzZY5bl5+drz549ioqK8mDLAAC48TBPM1COde/eXe+8847q1aun+vXra8WKFTp37pzatWvn6aYBwBU7e/asTp48aS6npqYqOTlZgYGBqlatmgdbBkg2wzAMTzcCQMmtWrVKy5Ytk9PpVGRkpAYPHqwGDRp4ulkAcMXi4+M1YcKEAuWxsbEaMWKEB1oE/A+hGQAAALDAmGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAAADAAqEZAAAAsEBoBgAAACwQmgEAAAALhGYAKKe+/fZbPfzww0pNTfV0UwDghkdoBgAAACzwNdoAUE7l5+crLy9P3t7estlsnm4OANzQCM0AAACABbunGwAAKJlvv/1WM2bM0PTp0xUSEqIRI0aodu3a6tmzpxYsWKAjR46ocuXK+sMf/qDY2Fi3bbOzs/XJJ59o8+bNysjIUFBQkJo0aaIBAwYoKChIkpSZmal///vf2rZtm3JychQWFqZu3bqpXbt25n5SU1M1cuRIPfLII/Lx8dHy5cvldDrVqFEjPfbYY6patao+/fRTrVmzRr/++quio6M1fPhwBQYGurVn+/btiouLU1JSkmw2mxo3bqxHHnlEtWvXvubXEQCKg9AMADeQkydPatq0aerQoYNiY2O1du1azZgxQ/Xq1TMD6NmzZzV27FgdO3ZM7du3V926dfXrr79qy5YtOnXqlIKCgnT+/HmNHz9eJ0+eVOfOnRUSEqIff/xRM2bMUE5Ojrp27ep23PXr1ysvL0+dO3dWVlaWli1bpn/+859q0qSJ9u7dqwceeEAnT57UqlWrtHDhQg0fPtzc9vvvv9c777yj6Oho9e/fX+fOndNXX32lsWPHasqUKQoJCbmu1xAACkNoBoAbyPHjxzVhwgQ1btxYktS6dWs9/vjjWrt2rQYMGCBJWrZsmVJSUjRmzBi1bNnS3LZ3795yjdhbs2aNjh07plGjRumuu+6SJN17770aP368Fi9erPbt28vf39/c9tSpU3rrrbcUEBAg6bfx1kuXLtX58+c1efJkVahQQZJ0+vRprV+/XsOGDZO3t7fOnj2refPmqUOHDvrLX/5i7i82NlZPPvmk4uLi3MoBwFOYPQMAbiDh4eFmYJakoKAghYWFuU1L99NPPykiIsItMLu4PlC4fft2ORwOtWnTxlxnt9vVpUsXnT17Vnv37nXbrlWrVmZglqQGDRpIku666y4zMLvK8/LydOrUKUnSrl27lJ2drTZt2uj06dPmPy8vLzVo0EDx8fFXczkAoNTQ0wwAN5Bq1aoVKKtYsaKys7PN5ZMnT+qOO+647H7S0tIUGhoqLy/3vpVatWqZ6y93XFeALqrc1Z4TJ05Ikl588cVC23FxbzYAeBKhGQBuIJeGXJdrPVFSUce1ao/r/5EjR8rhcBSod3EvNQB4EqEZAH5natasqZSUlMvWqV69ug4fPqz8/Hy34Hv8+HFzfWmoUaOGJCk4OFi33nprqewTAK4FxjQDwO/MHXfcocOHD2vTpk0F1rl6fps1ayan06kNGzaY6y5cuKCVK1fKz89PN998c6m0JTo6Wv7+/oqLi1NeXl6B9adPny6V4wDA1aKnGQB+Z3r06KEff/xRr7/+utq3b6969eopKytLW7Zs0bBhwxQZGamOHTvq66+/1owZM5SYmGhOObd//34NGjSo1MYaBwQEaNiwYXr77bf1j3/8Q23atFFQUJDS09O1bds2NWzYUH/+859L5VgAcDUIzQDwO+Pn56cXX3xRH3/8sTZt2qTvvvtOwcHBatKkiapWrSpJ8vHx0fjx4/Xhhx/qu+++05kzZxQWFqbhw4e7fblJaWjbtq0qV66spUuXatmyZcrNzVWVKlXUuHFjtW/fvlSPBQAlxddoAwAAABYY0wwAAABYIDQDAAAAFgjNAAAAgAVCMwAAAGCB0AwAAABYIDQDAAAAFgjNAAAAgAVCMwAAAGCB0AwAAABYIDQDAAAAFgjNAAAAgAVCMwAAAGDh/wMnLB/5MPEYJgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (8, 4))\n", + "plt.title(\"Values in target class after using SMOTETomek\")\n", + "sns.countplot(x = y_new)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.092711, + "end_time": "2021-05-22T12:30:34.361957", + "exception": false, + "start_time": "2021-05-22T12:30:34.269246", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "As we can see that we now have a balanced dataset, so we can model ahead with the model building part.\n", + "![](https://kiarofoods.com/wp-content/uploads/2019/10/line_break.png)\n", + "# Model Building:\n", + "Starting with the spliting of the training and testing data. For that, we check to see what is the best random state." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:30:34.556744Z", + "iopub.status.busy": "2021-05-22T12:30:34.555771Z", + "iopub.status.idle": "2021-05-22T12:31:36.726709Z", + "shell.execute_reply": "2021-05-22T12:31:36.727474Z", + "shell.execute_reply.started": "2021-05-22T11:38:47.158036Z" + }, + "papermill": { + "duration": 62.273404, + "end_time": "2021-05-22T12:31:36.727702", + "exception": false, + "start_time": "2021-05-22T12:30:34.454298", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Random State is 18, 81.59223607385198\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "max_accuracy = 0\n", + "best_rs = 0\n", + "for i in range(1, 150):\n", + " x_train, x_test, y_train, y_test = train_test_split(x_new, y_new, test_size = 0.30, random_state = i)\n", + " lg = LogisticRegression()\n", + " lg.fit(x_train, y_train)\n", + " pred = lg.predict(x_test)\n", + " acc = accuracy_score(y_test, pred)\n", + " if acc > max_accuracy: # after each iteration, acc is replace by the best possible accuracy\n", + " max_accuracy = acc\n", + " best_rs = i\n", + "print(f\"Best Random State is {best_rs}, {max_accuracy*100}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.094886, + "end_time": "2021-05-22T12:31:36.918331", + "exception": false, + "start_time": "2021-05-22T12:31:36.823445", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Best possible random state is 67, so using it to split the data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:31:37.112825Z", + "iopub.status.busy": "2021-05-22T12:31:37.112137Z", + "iopub.status.idle": "2021-05-22T12:31:37.122448Z", + "shell.execute_reply": "2021-05-22T12:31:37.121866Z", + "shell.execute_reply.started": "2021-05-22T11:40:52.558648Z" + }, + "papermill": { + "duration": 0.110651, + "end_time": "2021-05-22T12:31:37.122598", + "exception": false, + "start_time": "2021-05-22T12:31:37.011947", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x_new, y_new, test_size = 0.30, random_state = 67)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:31:37.315385Z", + "iopub.status.busy": "2021-05-22T12:31:37.314679Z", + "iopub.status.idle": "2021-05-22T12:31:37.323463Z", + "shell.execute_reply": "2021-05-22T12:31:37.322754Z", + "shell.execute_reply.started": "2021-05-22T11:40:54.899139Z" + }, + "papermill": { + "duration": 0.10701, + "end_time": "2021-05-22T12:31:37.323622", + "exception": false, + "start_time": "2021-05-22T12:31:37.216612", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.094132, + "end_time": "2021-05-22T12:31:37.512066", + "exception": false, + "start_time": "2021-05-22T12:31:37.417934", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Model Fitting:\n", + "Fitting 7 different models to check which model gives the best accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:31:37.713318Z", + "iopub.status.busy": "2021-05-22T12:31:37.709096Z", + "iopub.status.idle": "2021-05-22T12:32:27.322133Z", + "shell.execute_reply": "2021-05-22T12:32:27.321427Z", + "shell.execute_reply.started": "2021-05-22T11:41:50.858711Z" + }, + "papermill": { + "duration": 49.716197, + "end_time": "2021-05-22T12:32:27.322296", + "exception": false, + "start_time": "2021-05-22T12:31:37.606099", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score of Logistic Regression model is 81.18194729367208\n", + "Accuracy Score of Decision Tree Classifier model is 86.46047025406344\n", + "Accuracy Score of K-Nearest Neighbour Classifier model is 86.23954552627427\n", + "Accuracy Score of Support Vector Classifier model is 82.7836515701436\n", + "Accuracy Score of Random Forest model is 90.58702856241123\n", + "Accuracy Score of MultinomialNB model is 64.6836042291305\n", + "Accuracy Score of ADA Boost model is 84.9139971595392\n" + ] + } + ], + "source": [ + "# For Logistic Regression\n", + "lg = LogisticRegression()\n", + "lg.fit(x_train, y_train)\n", + "pred_lg = lg.predict(x_test)\n", + "print(\"Accuracy Score of Logistic Regression model is\", accuracy_score(y_test, pred_lg)*100)\n", + "\n", + "# For Decision Tree Classifier\n", + "dtc = DecisionTreeClassifier()\n", + "dtc.fit(x_train, y_train)\n", + "pred_dtc = dtc.predict(x_test)\n", + "print(\"Accuracy Score of Decision Tree Classifier model is\", accuracy_score(y_test, pred_dtc)*100)\n", + "\n", + "# For K-Nearest Neighbour Classifier\n", + "knc = KNeighborsClassifier(n_neighbors = 5)\n", + "knc.fit(x_train, y_train)\n", + "pred_knc = knc.predict(x_test)\n", + "print(\"Accuracy Score of K-Nearest Neighbour Classifier model is\", accuracy_score(y_test, pred_knc)*100)\n", + "\n", + "# For Support Vector Classifier\n", + "svc = SVC(kernel = 'rbf')\n", + "svc.fit(x_train, y_train)\n", + "pred_svc = svc.predict(x_test)\n", + "print(\"Accuracy Score of Support Vector Classifier model is\", accuracy_score(y_test, pred_svc)*100)\n", + "\n", + "# For Random Forest Classifier\n", + "rfc = RandomForestClassifier()\n", + "rfc.fit(x_train, y_train)\n", + "pred_rfc = rfc.predict(x_test)\n", + "print(\"Accuracy Score of Random Forest model is\", accuracy_score(y_test, pred_rfc)*100)\n", + "\n", + "# For MultinomialNB\n", + "nb = MultinomialNB() # making the Multinomial Naive Bayes class\n", + "nb.fit(x_train, y_train) # fitting the model\n", + "pred_nb = nb.predict(x_test) # predicting the values\n", + "print(\"Accuracy Score of MultinomialNB model is\", accuracy_score(y_test, pred_nb)*100)\n", + "\n", + "# For ADA Boost Classifier\n", + "ada= AdaBoostClassifier()\n", + "ada.fit(x_train, y_train) # fitting the model\n", + "pred_ada = ada.predict(x_test) # predicting the values\n", + "print(\"Accuracy Score of ADA Boost model is\", accuracy_score(y_test, pred_ada)*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.095308, + "end_time": "2021-05-22T12:32:27.512871", + "exception": false, + "start_time": "2021-05-22T12:32:27.417563", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Best accuracy score is given by Random Forest Classifier model. In order to avoid the bias and overfitting or underfitting, we cross validate the models and check the mean accuracy score of them.\n", + "## Cross Validation:\n", + "Cross validating the m,odels to see if they are underfitting or overfitting and to prevent bias. We will compare the mean accuracy scores of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:32:27.715604Z", + "iopub.status.busy": "2021-05-22T12:32:27.714808Z", + "iopub.status.idle": "2021-05-22T12:42:47.388711Z", + "shell.execute_reply": "2021-05-22T12:42:47.388122Z", + "shell.execute_reply.started": "2021-05-22T11:43:43.797596Z" + }, + "papermill": { + "duration": 619.78021, + "end_time": "2021-05-22T12:42:47.388865", + "exception": false, + "start_time": "2021-05-22T12:32:27.608655", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.74745562 0.78911243 0.79857988 0.80804734 0.81017751 0.81278107\n", + " 0.82054924 0.8186553 0.82410038 0.81060606]\n", + "Mean of accuracy scores is for Logistic Regression is 80.40064831002331\n", + "\n", + "[0.67029586 0.74130178 0.74579882 0.90414201 0.91810651 0.90982249\n", + " 0.91548295 0.90932765 0.90980114 0.91098485]\n", + "Mean of accuracy scores is for Decision Tree Classifier is 85.35064046530391\n", + "\n", + "[0.82721893 0.85112426 0.85633136 0.87100592 0.87266272 0.87857988\n", + " 0.88494318 0.87571023 0.88517992 0.87689394]\n", + "Mean of accuracy scores is for KNN Classifier is 86.79650349650349\n", + "\n", + "[0.78224852 0.82272189 0.82035503 0.8243787 0.82934911 0.8364497\n", + " 0.84161932 0.83285985 0.83806818 0.8359375 ]\n", + "Mean of accuracy scores is for SVC Classifier is 82.6398780706473\n", + "\n", + "[0.70911243 0.80331361 0.81467456 0.93680473 0.94674556 0.93609467\n", + " 0.9547822 0.94389205 0.95004735 0.94389205]\n", + "Mean of accuracy scores is for Random Forest Classifier is 89.39359198493813\n", + "\n", + "[0.63029586 0.58721893 0.60284024 0.66366864 0.65680473 0.66792899\n", + " 0.65648674 0.65767045 0.65482955 0.66998106]\n", + "Mean of accuracy scores is for MultinomialNB is 64.47725199480007\n", + "\n", + "[0.69183432 0.79739645 0.81278107 0.86059172 0.86295858 0.86461538\n", + " 0.88091856 0.8671875 0.87286932 0.8577178 ]\n", + "Mean of accuracy scores is for ADA Boost Classifier is 83.68870696611081\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "\n", + "lg_scores = cross_val_score(lg, x_new, y_new, cv = 10) # cross validating the model\n", + "print(lg_scores) # accuracy scores of each cross validation cycle\n", + "print(f\"Mean of accuracy scores is for Logistic Regression is {lg_scores.mean()*100}\\n\")\n", + "\n", + "dtc_scores = cross_val_score(dtc, x_new, y_new, cv = 10)\n", + "print(dtc_scores)\n", + "print(f\"Mean of accuracy scores is for Decision Tree Classifier is {dtc_scores.mean()*100}\\n\")\n", + "\n", + "knc_scores = cross_val_score(knc, x_new, y_new, cv = 10)\n", + "print(knc_scores)\n", + "print(f\"Mean of accuracy scores is for KNN Classifier is {knc_scores.mean()*100}\\n\")\n", + "\n", + "svc_scores = cross_val_score(svc, x_new, y_new, cv = 10)\n", + "print(svc_scores)\n", + "print(f\"Mean of accuracy scores is for SVC Classifier is {svc_scores.mean()*100}\\n\")\n", + "\n", + "rfc_scores = cross_val_score(rfc, x_new, y_new, cv = 10)\n", + "print(rfc_scores)\n", + "print(f\"Mean of accuracy scores is for Random Forest Classifier is {rfc_scores.mean()*100}\\n\")\n", + "\n", + "nb_scores = cross_val_score(nb, x_new, y_new, cv = 10)\n", + "print(nb_scores)\n", + "print(f\"Mean of accuracy scores is for MultinomialNB is {nb_scores.mean()*100}\\n\")\n", + "\n", + "ada_scores = cross_val_score(ada, x_new, y_new, cv = 10)\n", + "print(ada_scores)\n", + "print(f\"Mean of accuracy scores is for ADA Boost Classifier is {ada_scores.mean()*100}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:42:47.594281Z", + "iopub.status.busy": "2021-05-22T12:42:47.593497Z", + "iopub.status.idle": "2021-05-22T12:42:47.609643Z", + "shell.execute_reply": "2021-05-22T12:42:47.608883Z", + "shell.execute_reply.started": "2021-05-22T11:59:56.168200Z" + }, + "papermill": { + "duration": 0.12282, + "end_time": "2021-05-22T12:42:47.609856", + "exception": false, + "start_time": "2021-05-22T12:42:47.487036", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logistic Regression 0.7812989836487674\n", + "Decision Tree Classifier 1.1098297887595265\n", + "KNeighbors Classifier -0.5569579702292202\n", + "SVC 0.14377349949630513\n", + "Random Forest Classifier 1.1934365774731077\n", + "MultinomialNB 0.20635223433042427\n", + "ADA Boost Classifier 1.2252901934283926\n" + ] + } + ], + "source": [ + "# Checking for difference between accuracy and mean accuracies.\n", + "lis3 = ['Logistic Regression','Decision Tree Classifier','KNeighbors Classifier','SVC', 'Random Forest Classifier', \n", + " 'MultinomialNB', 'ADA Boost Classifier']\n", + "\n", + "lis1 = [accuracy_score(y_test, pred_lg)*100, accuracy_score(y_test, pred_dtc)*100, accuracy_score(y_test, pred_knc)*100, \n", + " accuracy_score(y_test, pred_svc)*100, accuracy_score(y_test, pred_rfc)*100, accuracy_score(y_test, pred_nb)*100,\n", + " accuracy_score(y_test, pred_ada)*100]\n", + "\n", + "lis2 = [lg_scores.mean()*100, dtc_scores.mean()*100, knc_scores.mean()*100, svc_scores.mean()*100, rfc_scores.mean()*100, \n", + " nb_scores.mean()*100, ada_scores.mean()*100]\n", + "\n", + "for i in range(0, 7):\n", + " dif = (lis1[i]) - (lis2[i])\n", + " print(lis3[i], dif)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.098634, + "end_time": "2021-05-22T12:42:47.807472", + "exception": false, + "start_time": "2021-05-22T12:42:47.708838", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Random forest classifier is the best model with highest cross validation mean score and accuracy score**. We will use it for the model building.\n", + "## Hyperparameter Tuning:\n", + "Tuning the parameters of the Random Forest in order to obtain the best possible parameters for model building." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:42:48.017644Z", + "iopub.status.busy": "2021-05-22T12:42:48.016585Z", + "iopub.status.idle": "2021-05-22T12:54:44.040321Z", + "shell.execute_reply": "2021-05-22T12:54:44.040849Z", + "shell.execute_reply.started": "2021-05-22T12:00:46.048143Z" + }, + "papermill": { + "duration": 716.132052, + "end_time": "2021-05-22T12:54:44.041069", + "exception": false, + "start_time": "2021-05-22T12:42:47.909017", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9065754586536448\n", + "{'criterion': 'entropy', 'min_samples_split': 2, 'n_estimators': 200}\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "rfc = RandomForestClassifier()\n", + "param = dict()\n", + "param['criterion'] = ['gini', 'entropy']\n", + "param['n_estimators'] = [1, 2, 4, 8, 10, 16, 32, 64, 100, 200]\n", + "param['min_samples_split'] = [1,2,5,8,10,15,20,25,50,55,60,80,100]\n", + "\n", + "\n", + "gs = GridSearchCV(estimator = rfc, param_grid = param, scoring='f1', cv = 5, n_jobs = 3)\n", + "gs.fit(x_train, y_train)\n", + "print(gs.best_score_)\n", + "print(gs.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.118433, + "end_time": "2021-05-22T12:54:44.314842", + "exception": false, + "start_time": "2021-05-22T12:54:44.196409", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "After the hyperparameter tuning, **the best parameters for Random Forest Classifier are 'crietrion' = 'entropy', 'min_samples_split' = 2, 'n_estimators' = 100**. We build the model using these parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:54:44.521557Z", + "iopub.status.busy": "2021-05-22T12:54:44.520564Z", + "iopub.status.idle": "2021-05-22T12:54:50.211701Z", + "shell.execute_reply": "2021-05-22T12:54:50.210881Z", + "shell.execute_reply.started": "2021-05-22T12:24:51.545290Z" + }, + "papermill": { + "duration": 5.792616, + "end_time": "2021-05-22T12:54:50.211852", + "exception": false, + "start_time": "2021-05-22T12:54:44.419236", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "rfc = RandomForestClassifier(criterion = 'entropy', min_samples_split = 2, n_estimators = 100)\n", + "rfc.fit(x_train, y_train)\n", + "print(rfc.score(x_train, y_train))\n", + "pred_rfc = rfc.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.099384, + "end_time": "2021-05-22T12:54:50.410658", + "exception": false, + "start_time": "2021-05-22T12:54:50.311274", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "![](https://kiarofoods.com/wp-content/uploads/2019/10/line_break.png)\n", + "# Model Evaluation:\n", + "We have build the model after the cross validation and hyper parameter tuning. It is now time to evaluate the model using the classification report, confusion matrix and ROC curve." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "_kg_hide-input": true, + "execution": { + "iopub.execute_input": "2021-05-22T12:54:50.617109Z", + "iopub.status.busy": "2021-05-22T12:54:50.616068Z", + "iopub.status.idle": "2021-05-22T12:54:51.108082Z", + "shell.execute_reply": "2021-05-22T12:54:51.107436Z", + "shell.execute_reply.started": "2021-05-22T12:25:23.573098Z" + }, + "papermill": { + "duration": 0.598141, + "end_time": "2021-05-22T12:54:51.108226", + "exception": false, + "start_time": "2021-05-22T12:54:50.510085", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score of RFC model is 90.69749092630582\n", + "Confusion matrix for RFC Model is\n", + "[[5696 638]\n", + " [ 541 5799]]\n", + "Classification Report of the RFC Model is\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.90 0.91 6334\n", + " 1 0.90 0.91 0.91 6340\n", + "\n", + " accuracy 0.91 12674\n", + " macro avg 0.91 0.91 0.91 12674\n", + "weighted avg 0.91 0.91 0.91 12674\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import plot_roc_curve\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "\n", + "print(\"Accuracy Score of RFC model is\", accuracy_score(y_test, pred_rfc)*100)\n", + "print(\"Confusion matrix for RFC Model is\")\n", + "print(confusion_matrix(y_test, pred_rfc))\n", + "print(\"Classification Report of the RFC Model is\")\n", + "print(classification_report(y_test, pred_rfc))\n", + "\n", + "plot_roc_curve(rfc, x_test, y_test) # arg. are model name, feature testing data, label testing data.\n", + "plt.title(\"Recevier's Operating Characteristic\")\n", + "plt.xlabel(\"False Positive Rate\")\n", + "plt.ylabel(\"True Positive Rate\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.101577, + "end_time": "2021-05-22T12:54:51.310273", + "exception": false, + "start_time": "2021-05-22T12:54:51.208696", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "After the model evaluation , we get the **precision and recall for both the target variable as 0.92 and 0.91**. The **f1- score of the model is 0.92**. The ROC curve gave us **the AUC score which is 0.98**. Model evaluation gives the results that ***the prediction is very accurate.***\n", + "![](https://miro.medium.com/max/2400/1*IH10jlQEJ7GW1_oq8s7WPw.png)\n", + "# Serialisation:\n", + "Now we save the Random Forest Classifier Model as an object using joblib." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "execution": { + "iopub.execute_input": "2021-05-22T12:54:51.522751Z", + "iopub.status.busy": "2021-05-22T12:54:51.521599Z", + "iopub.status.idle": "2021-05-22T12:54:51.629887Z", + "shell.execute_reply": "2021-05-22T12:54:51.629272Z", + "shell.execute_reply.started": "2021-05-22T12:27:09.000988Z" + }, + "papermill": { + "duration": 0.217902, + "end_time": "2021-05-22T12:54:51.630062", + "exception": false, + "start_time": "2021-05-22T12:54:51.412160", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Census Income Prediction.obj']" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import joblib\n", + "joblib.dump(rfc, 'Census Income Prediction.obj') # saving the model as an object" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "papermill": { + "duration": 0.101546, + "end_time": "2021-05-22T12:54:51.835095", + "exception": false, + "start_time": "2021-05-22T12:54:51.733549", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Fairness" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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": 51, + "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": 52, + "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": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age fnlwgt education.num capital.gain capital.loss \\\n", + "0 0.616438 0.296903 0.769231 0.0 1.00000 \n", + "1 0.753425 0.265630 1.000000 0.0 1.00000 \n", + "2 0.671233 0.684115 0.538462 0.0 1.00000 \n", + "3 0.219178 0.504404 0.461538 0.0 0.90461 \n", + "4 0.164384 0.252780 0.461538 0.0 0.90461 \n", + "\n", + " hours.per.week workclass_enc occupation_enc country_enc sex_enc \\\n", + "0 0.464789 0.064279 0.122729 1 0 \n", + "1 0.492958 0.064279 0.132365 1 1 \n", + "2 0.211268 0.697030 0.051989 1 1 \n", + "3 0.492958 0.064279 0.122729 1 0 \n", + "4 0.492958 0.697030 0.118823 1 0 \n", + "\n", + " marital_status_enc race_enc relationship_enc income \n", + "0 0.466795 0.856208 0.046715 0 \n", + "1 0.466795 0.856208 0.413569 0 \n", + "2 0.466795 0.856208 0.413569 0 \n", + "3 0.138616 0.856208 0.147308 0 \n", + "4 0.322677 0.094700 0.106485 0 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##train['Sex'] = train['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n", + "x_new['income'] = y_new\n", + "x_new.head()\n", + "#features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\", \"Survived\"]\n", + "#X = pd.get_dummies(train_data[features])" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "privileged_groups = [{'sex_enc': 1}]\n", + "unprivileged_groups = [{'sex_enc': 0}]\n", + "dataset_orig = StandardDataset(x_new,\n", + " label_name='income',\n", + " protected_attribute_names=['sex_enc'],\n", + " favorable_classes=[1],\n", + " privileged_classes=[[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "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 = 100\n", + "\n", + "model = AdaBoostClassifier(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": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from csv import writer\n", + "from sklearn.metrics import accuracy_score, f1_score\n", + "\n", + "final_metrics = []\n", + "accuracy = []\n", + "f1= []\n", + "\n", + "for i in range(1,num_estimators+1):\n", + " \n", + " model = AdaBoostClassifier(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": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4\n", + "0 -0.489667 -0.274678 0.333797 0.326917 0.114503\n", + "1 -0.489667 -0.274678 0.333797 0.326917 0.114503\n", + "2 -0.476012 -0.207990 0.300087 0.356299 0.098364\n", + "3 -0.463112 -0.223016 0.285508 0.343126 0.103231\n", + "4 -0.328021 -0.173282 0.167031 0.363957 0.178846\n", + ".. ... ... ... ... ...\n", + "95 -0.382102 -0.147623 0.170351 0.382188 0.090602\n", + "96 -0.385429 -0.152675 0.173799 0.378979 0.089444\n", + "97 -0.379574 -0.143573 0.166849 0.384339 0.090467\n", + "98 -0.383776 -0.152230 0.172284 0.379699 0.089998\n", + "99 -0.380557 -0.146509 0.168575 0.382854 0.090855\n", + "\n", + "[100 rows x 5 columns]\n" + ] + }, + { + "data": { + "text/html": [ + "
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classifierT0T1T2T3T4T5T6T7T8...T90T91T92T93T94T95T96T97T98T99
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f10.8630290.7908750.7908750.8065270.8144800.7728660.8393380.8390310.8191080.830235...0.8623010.8621950.8626140.8637600.8637100.8627540.8638550.8635160.8637320.863029
statistical_parity_difference-0.380557-0.489667-0.489667-0.476012-0.463112-0.328021-0.412997-0.424547-0.385264-0.381616...-0.385786-0.381909-0.385429-0.387233-0.383160-0.382102-0.385429-0.379574-0.383776-0.380557
equal_opportunity_difference-0.146509-0.274678-0.274678-0.207990-0.223016-0.173282-0.135784-0.184808-0.210735-0.152325...-0.150149-0.146768-0.151190-0.153603-0.150818-0.147623-0.152675-0.143573-0.152230-0.146509
average_abs_odds_difference0.1685750.3337970.3337970.3000870.2855080.1670310.2101320.2298610.2073660.188492...0.1743450.1703500.1740900.1756290.1714490.1703510.1737990.1668490.1722840.168575
disparate_impact-0.960102-1.118050-1.118050-1.031986-1.069658-1.010719-0.917348-0.990932-1.045965-0.933028...-0.970081-0.958494-0.970275-0.973950-0.966613-0.961843-0.970275-0.956230-0.968377-0.960102
theil_index0.0908550.1145030.1145030.0983640.1032310.1788460.0871910.0925860.1286840.109787...0.0903490.0906700.0903050.0891900.0900620.0906020.0894440.0904670.0899980.090855
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" + ], + "text/plain": [ + " classifier T0 T1 T2 \\\n", + "accuracy 0.861107 0.770929 0.770929 0.785039 \n", + "f1 0.863029 0.790875 0.790875 0.806527 \n", + "statistical_parity_difference -0.380557 -0.489667 -0.489667 -0.476012 \n", + "equal_opportunity_difference -0.146509 -0.274678 -0.274678 -0.207990 \n", + "average_abs_odds_difference 0.168575 0.333797 0.333797 0.300087 \n", + "disparate_impact -0.960102 -1.118050 -1.118050 -1.031986 \n", + "theil_index 0.090855 0.114503 0.114503 0.098364 \n", + "\n", + " T3 T4 T5 T6 \\\n", + "accuracy 0.799622 0.789374 0.827053 0.829261 \n", + "f1 0.814480 0.772866 0.839338 0.839031 \n", + "statistical_parity_difference -0.463112 -0.328021 -0.412997 -0.424547 \n", + "equal_opportunity_difference -0.223016 -0.173282 -0.135784 -0.184808 \n", + "average_abs_odds_difference 0.285508 0.167031 0.210132 0.229861 \n", + "disparate_impact -1.069658 -1.010719 -0.917348 -0.990932 \n", + "theil_index 0.103231 0.178846 0.087191 0.092586 \n", + "\n", + " T7 T8 ... T90 T91 \\\n", + "accuracy 0.820905 0.825792 ... 0.859924 0.859924 \n", + "f1 0.819108 0.830235 ... 0.862301 0.862195 \n", + "statistical_parity_difference -0.385264 -0.381616 ... -0.385786 -0.381909 \n", + "equal_opportunity_difference -0.210735 -0.152325 ... -0.150149 -0.146768 \n", + "average_abs_odds_difference 0.207366 0.188492 ... 0.174345 0.170350 \n", + "disparate_impact -1.045965 -0.933028 ... -0.970081 -0.958494 \n", + "theil_index 0.128684 0.109787 ... 0.090349 0.090670 \n", + "\n", + " T92 T93 T94 T95 \\\n", + "accuracy 0.860318 0.861343 0.861659 0.860634 \n", + "f1 0.862614 0.863760 0.863710 0.862754 \n", + "statistical_parity_difference -0.385429 -0.387233 -0.383160 -0.382102 \n", + "equal_opportunity_difference -0.151190 -0.153603 -0.150818 -0.147623 \n", + "average_abs_odds_difference 0.174090 0.175629 0.171449 0.170351 \n", + "disparate_impact -0.970275 -0.973950 -0.966613 -0.961843 \n", + "theil_index 0.090305 0.089190 0.090062 0.090602 \n", + "\n", + " T96 T97 T98 T99 \n", + "accuracy 0.861580 0.861580 0.861659 0.861107 \n", + "f1 0.863855 0.863516 0.863732 0.863029 \n", + "statistical_parity_difference -0.385429 -0.379574 -0.383776 -0.380557 \n", + "equal_opportunity_difference -0.152675 -0.143573 -0.152230 -0.146509 \n", + "average_abs_odds_difference 0.173799 0.166849 0.172284 0.168575 \n", + "disparate_impact -0.970275 -0.956230 -0.968377 -0.960102 \n", + "theil_index 0.089444 0.090467 0.089998 0.090855 \n", + "\n", + "[7 rows x 101 columns]" + ] + }, + "execution_count": 57, + "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": { + "default_parameters": {}, + "duration": 1493.075966, + "end_time": "2021-05-22T12:54:52.947554", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2021-05-22T12:29:59.871588", + "version": "2.3.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}