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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "18dba6f5",
+ "metadata": {},
+ "source": [
+ "# IBM HR Analytics Employee Attrition & Performance\n",
+ "Predict attrition of your valuable employees\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6371c2d",
+ "metadata": {},
+ "source": [
+ "https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "id": "f67961e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import copy\n",
+ "import time\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "from collections import Counter\n",
+ "\n",
+ "import pickle\n",
+ "import sklearn\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.metrics import classification_report\n",
+ "from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,\n",
+ " GradientBoostingClassifier)\n",
+ "\n",
+ "\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn import svm\n",
+ "from sklearn.model_selection import StratifiedKFold,KFold\n",
+ "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
+ "from sklearn.datasets import make_moons, make_circles, make_classification\n",
+ "from sklearn.linear_model import LogisticRegressionCV,LogisticRegression,SGDClassifier,PassiveAggressiveClassifier,Perceptron\n",
+ "from sklearn.naive_bayes import MultinomialNB\n",
+ "from sklearn.neural_network import MLPClassifier\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.gaussian_process import GaussianProcessClassifier\n",
+ "from sklearn.gaussian_process.kernels import RBF\n",
+ "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,ExtraTreesClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.naive_bayes import GaussianNB\n",
+ "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
+ "\n",
+ "\n",
+ "from sklearn.metrics import f1_score,accuracy_score\n",
+ "from sklearn.model_selection import RandomizedSearchCV\n",
+ "import xgboost\n",
+ "import catboost as cb\n",
+ "from catboost import CatBoostClassifier, Pool\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "\n",
+ "\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "from torch.optim import lr_scheduler\n",
+ "import numpy as np\n",
+ "import torchvision\n",
+ "from torchvision import datasets, models, transforms\n",
+ "\n",
+ "\n",
+ "import seaborn as sns\n",
+ "sns.set_theme()\n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.ion() # interactive mode\n",
+ "%matplotlib inline\n",
+ "from IPython.display import Image\n",
+ "\n",
+ "import keras\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense\n",
+ "from keras.layers import LeakyReLU, PReLU, ELU\n",
+ "from keras.layers import Dropout\n",
+ "from matplotlib import pyplot\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "from keras.layers import LSTM, GRU\n",
+ "from keras.callbacks import EarlyStopping\n",
+ "from keras.layers import Activation\n",
+ "\n",
+ "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+ "# data.head(2)\n",
+ "\n",
+ "import sklearn\n",
+ "import numpy as np\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import pandas as pd\n",
+ "import seaborn as sns; sns.set_theme()\n",
+ "import matplotlib.pyplot as plt\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "id": "a82a5479",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Train=pd.read_csv(\".\\\\employee_attrition.csv\",na_values=[])\n",
+ "Train=Train.drop(\"Over18\",axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bd17b48",
+ "metadata": {},
+ "source": [
+ "# EDA (Exploratory Data Analysis)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "id": "dc408e53",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7ea8e8d331604637ac4dcc16429cee2f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " | | [ 0%] 00:00 ->…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Report sweetviz_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import sweetviz as sv\n",
+ "\n",
+ "#EDA using Autoviz\n",
+ "sweet_report = sv.analyze(pd.read_csv(\"./employee_attrition.csv\"))\n",
+ "\n",
+ "#Saving results to HTML file\n",
+ "sweet_report.show_html('sweetviz_report.html')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "id": "a670b7b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e46cdcad899a40d9865f3e3f9811034e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Summarize dataset: 0%| | 0/42 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "13032e7b517c453495aac1532d6d7dd8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Generate report structure: 0%| | 0/1 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d4ff4b425a7d442f99bc36a57d7e3181",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Render HTML: 0%| | 0/1 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cce14bc5827c4bb0ba6ed6fa2d5a17c8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Export report to file: 0%| | 0/1 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from pandas_profiling import ProfileReport\n",
+ "\n",
+ "profile = ProfileReport(Train, title='IBM_HR_Employee_Attrition_Report',minimal=True, explorative = True)\n",
+ "profile.to_file('IBM_HR_Employee_Attrition_Profil.html')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "id": "0684db07",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "2 37 Yes Travel_Rarely 1373 Research & Development \n",
+ "3 33 No Travel_Frequently 1392 Research & Development \n",
+ "4 27 No Travel_Rarely 591 Research & Development \n",
+ "... ... ... ... ... ... \n",
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+ "1469 34 No Travel_Rarely 628 Research & Development \n",
+ "\n",
+ " DistanceFromHome Education EducationField EmployeeCount \\\n",
+ "0 1 2 Life Sciences 1 \n",
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+ "2 2 2 Other 1 \n",
+ "3 3 4 Life Sciences 1 \n",
+ "4 2 1 Medical 1 \n",
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+ "\n",
+ " EmployeeNumber ... RelationshipSatisfaction StandardHours \\\n",
+ "0 1 ... 1 80 \n",
+ "1 2 ... 4 80 \n",
+ "2 4 ... 2 80 \n",
+ "3 5 ... 3 80 \n",
+ "4 7 ... 4 80 \n",
+ "... ... ... ... ... \n",
+ "1465 2061 ... 3 80 \n",
+ "1466 2062 ... 1 80 \n",
+ "1467 2064 ... 2 80 \n",
+ "1468 2065 ... 4 80 \n",
+ "1469 2068 ... 1 80 \n",
+ "\n",
+ " StockOptionLevel TotalWorkingYears TrainingTimesLastYear \\\n",
+ "0 0 8 0 \n",
+ "1 1 10 3 \n",
+ "2 0 7 3 \n",
+ "3 0 8 3 \n",
+ "4 1 6 3 \n",
+ "... ... ... ... \n",
+ "1465 1 17 3 \n",
+ "1466 1 9 5 \n",
+ "1467 1 6 0 \n",
+ "1468 0 17 3 \n",
+ "1469 0 6 3 \n",
+ "\n",
+ " WorkLifeBalance YearsAtCompany YearsInCurrentRole \\\n",
+ "0 1 6 4 \n",
+ "1 3 10 7 \n",
+ "2 3 0 0 \n",
+ "3 3 8 7 \n",
+ "4 3 2 2 \n",
+ "... ... ... ... \n",
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+ "1466 3 7 7 \n",
+ "1467 3 6 2 \n",
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+ "\n",
+ " YearsSinceLastPromotion YearsWithCurrManager \n",
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+ "3 3 0 \n",
+ "4 2 2 \n",
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+ "\n",
+ "[1470 rows x 34 columns]"
+ ]
+ },
+ "execution_count": 161,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "id": "470729a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Train=Train.drop([\"EmployeeNumber\"],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b0c565aa",
+ "metadata": {},
+ "source": [
+ "# Pre-Processing (Encoding & Normalization)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "id": "89014c9e",
+ "metadata": {},
+ "outputs": [
+ {
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+ "2 2 4 1 4 ... \n",
+ "3 4 1 1 4 ... \n",
+ "4 1 3 1 1 ... \n",
+ "\n",
+ " RelationshipSatisfaction StandardHours StockOptionLevel \\\n",
+ "0 1 80 0 \n",
+ "1 4 80 1 \n",
+ "2 2 80 0 \n",
+ "3 3 80 0 \n",
+ "4 4 80 1 \n",
+ "\n",
+ " TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany \\\n",
+ "0 8 0 1 6 \n",
+ "1 10 3 3 10 \n",
+ "2 7 3 3 0 \n",
+ "3 8 3 3 8 \n",
+ "4 6 3 3 2 \n",
+ "\n",
+ " YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager \n",
+ "0 4 0 5 \n",
+ "1 7 1 7 \n",
+ "2 0 0 0 \n",
+ "3 7 3 0 \n",
+ "4 2 2 2 \n",
+ "\n",
+ "[5 rows x 33 columns]"
+ ]
+ },
+ "execution_count": 163,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def label(df):\n",
+ " df1 = df.select_dtypes(include=['object'])\n",
+ " for i in df.columns:\n",
+ " if df[i].dtypes == 'object': \n",
+ " labelencoder = LabelEncoder()\n",
+ " df[i] = labelencoder.fit_transform(df[i])\n",
+ " return df \n",
+ "\n",
+ "Train=label(Train)\n",
+ "Train.head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "id": "053723eb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def means(df):\n",
+ " df1=df.columns\n",
+ " my_imputer = SimpleImputer()\n",
+ " imputed_X= pd.DataFrame(my_imputer.fit_transform(df))\n",
+ " df= pd.DataFrame(my_imputer.transform(df))\n",
+ " df=MinMaxScaler().fit_transform(df)\n",
+ " df=pd.DataFrame(df)\n",
+ " df.columns=df1\n",
+ " return df \n",
+ " \n",
+ "# Train=means(Train) \n",
+ "# Train.head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 165,
+ "id": "bf6268ea",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "uniform_data = Train.corr()\n",
+ "ax = plt.subplots(figsize=(20,20))\n",
+ "annot_kws={'fontsize':10, \n",
+ " 'fontstyle':'italic', \n",
+ " 'color':\"k\",\n",
+ " 'alpha':0.5, \n",
+ " 'rotation':\"vertical\",\n",
+ " 'verticalalignment':'center',\n",
+ " 'backgroundcolor':'w'}\n",
+ "ax=sns.heatmap(uniform_data,annot=False, annot_kws= annot_kws,fmt=\"f\",vmin=0, vmax=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "id": "9ec9eb4d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Y=Train[\"Attrition\"]\n",
+ "Train=Train.drop(\"Attrition\",axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dcaafb25",
+ "metadata": {},
+ "source": [
+ "# Feature Enginnering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "id": "5be5780a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " ['OverTime', 'JobLevel', 'StockOptionLevel', 'TotalWorkingYears', 'MonthlyIncome', 'MaritalStatus', 'YearsWithCurrManager', 'Department', 'JobRole', 'EnvironmentSatisfaction', 'YearsAtCompany', 'JobInvolvement', 'Age', 'JobSatisfaction', 'WorkLifeBalance', 'NumCompaniesWorked', 'YearsInCurrentRole', 'DistanceFromHome', 'BusinessTravel', 'YearsSinceLastPromotion', 'HourlyRate', 'RelationshipSatisfaction', 'DailyRate', 'EducationField', 'PercentSalaryHike', 'MonthlyRate', 'TrainingTimesLastYear', 'Education', 'Gender', 'PerformanceRating', 'StandardHours', 'EmployeeCount']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Features \n",
+ " avg \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " OverTime \n",
+ " 273.20 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " JobLevel \n",
+ " 81.99 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " StockOptionLevel \n",
+ " 41.80 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " TotalWorkingYears \n",
+ " 40.53 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " MonthlyIncome \n",
+ " 37.43 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " MaritalStatus \n",
+ " 31.21 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " YearsWithCurrManager \n",
+ " 29.10 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " Department \n",
+ " 29.03 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " JobRole \n",
+ " 26.58 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " EnvironmentSatisfaction \n",
+ " 26.23 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " YearsAtCompany \n",
+ " 24.88 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " JobInvolvement \n",
+ " 24.76 \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " Age \n",
+ " 23.92 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " JobSatisfaction \n",
+ " 23.28 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " WorkLifeBalance \n",
+ " 22.71 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " NumCompaniesWorked \n",
+ " 22.43 \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " YearsInCurrentRole \n",
+ " 22.26 \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " DistanceFromHome \n",
+ " 22.17 \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " BusinessTravel \n",
+ " 20.61 \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " YearsSinceLastPromotion \n",
+ " 19.75 \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " HourlyRate \n",
+ " 19.37 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " RelationshipSatisfaction \n",
+ " 18.69 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " DailyRate \n",
+ " 17.71 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " EducationField \n",
+ " 16.86 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " PercentSalaryHike \n",
+ " 15.72 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " MonthlyRate \n",
+ " 14.53 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " TrainingTimesLastYear \n",
+ " 14.44 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Education \n",
+ " 14.11 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Gender \n",
+ " 13.40 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " PerformanceRating \n",
+ " 11.31 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " StandardHours \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " EmployeeCount \n",
+ " 0.00 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Features avg\n",
+ "0 OverTime 273.20\n",
+ "1 JobLevel 81.99\n",
+ "2 StockOptionLevel 41.80\n",
+ "3 TotalWorkingYears 40.53\n",
+ "4 MonthlyIncome 37.43\n",
+ "5 MaritalStatus 31.21\n",
+ "6 YearsWithCurrManager 29.10\n",
+ "7 Department 29.03\n",
+ "8 JobRole 26.58\n",
+ "9 EnvironmentSatisfaction 26.23\n",
+ "10 YearsAtCompany 24.88\n",
+ "11 JobInvolvement 24.76\n",
+ "12 Age 23.92\n",
+ "13 JobSatisfaction 23.28\n",
+ "14 WorkLifeBalance 22.71\n",
+ "15 NumCompaniesWorked 22.43\n",
+ "16 YearsInCurrentRole 22.26\n",
+ "17 DistanceFromHome 22.17\n",
+ "18 BusinessTravel 20.61\n",
+ "19 YearsSinceLastPromotion 19.75\n",
+ "20 HourlyRate 19.37\n",
+ "21 RelationshipSatisfaction 18.69\n",
+ "22 DailyRate 17.71\n",
+ "23 EducationField 16.86\n",
+ "24 PercentSalaryHike 15.72\n",
+ "25 MonthlyRate 14.53\n",
+ "26 TrainingTimesLastYear 14.44\n",
+ "27 Education 14.11\n",
+ "28 Gender 13.40\n",
+ "29 PerformanceRating 11.31\n",
+ "30 StandardHours 0.00\n",
+ "31 EmployeeCount 0.00"
+ ]
+ },
+ "execution_count": 167,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "selector=xgboost.XGBClassifier(n_estimators= 100,verbosity=0, max_depth= 40, learning_rate= 0.01, gamma= 0.07, colsample_bytree= 0.6)\n",
+ "selector.fit(Train, Y)\n",
+ "feature_imp = selector.feature_importances_\n",
+ "\n",
+ "\n",
+ "from collections import Counter,defaultdict\n",
+ "a=[]\n",
+ "b=[]\n",
+ "for index, val in enumerate(feature_imp):\n",
+ " temp=round((val * 1000), 2)\n",
+ " a.append(Train.columns[index])\n",
+ " b.append(temp)\n",
+ "t=pd.DataFrame()\n",
+ "t[\"Features\"]=a\n",
+ "t[\"avg\"]=b\n",
+ "t=t.sort_values(by = 'avg',ascending=False)\n",
+ "print(\"\\n\",list(t.Features))\n",
+ "t.reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "id": "f1b9f9d3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " YearsWithCurrManager \n",
+ " Department \n",
+ " JobRole \n",
+ " EnvironmentSatisfaction \n",
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+ " DailyRate \n",
+ " EducationField \n",
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+ "
1470 rows × 32 columns
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+ "
"
+ ],
+ "text/plain": [
+ " OverTime JobLevel StockOptionLevel TotalWorkingYears MonthlyIncome \\\n",
+ "0 1 2 0 8 5993 \n",
+ "1 0 2 1 10 5130 \n",
+ "2 1 1 0 7 2090 \n",
+ "3 1 1 0 8 2909 \n",
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+ "1467 1 2 1 6 6142 \n",
+ "1468 0 2 0 17 5390 \n",
+ "1469 0 2 0 6 4404 \n",
+ "\n",
+ " MaritalStatus YearsWithCurrManager Department JobRole \\\n",
+ "0 2 5 2 7 \n",
+ "1 1 7 1 6 \n",
+ "2 2 0 1 2 \n",
+ "3 1 0 1 6 \n",
+ "4 1 2 1 2 \n",
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+ "1466 1 7 1 0 \n",
+ "1467 1 3 1 4 \n",
+ "1468 1 8 2 7 \n",
+ "1469 1 2 1 2 \n",
+ "\n",
+ " EnvironmentSatisfaction ... DailyRate EducationField \\\n",
+ "0 2 ... 1102 1 \n",
+ "1 3 ... 279 1 \n",
+ "2 4 ... 1373 4 \n",
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+ "1466 4 ... 613 3 \n",
+ "1467 2 ... 155 1 \n",
+ "1468 4 ... 1023 3 \n",
+ "1469 2 ... 628 3 \n",
+ "\n",
+ " PercentSalaryHike MonthlyRate TrainingTimesLastYear Education \\\n",
+ "0 11 19479 0 2 \n",
+ "1 23 24907 3 1 \n",
+ "2 15 2396 3 2 \n",
+ "3 11 23159 3 4 \n",
+ "4 12 16632 3 1 \n",
+ "... ... ... ... ... \n",
+ "1465 17 12290 3 2 \n",
+ "1466 15 21457 5 1 \n",
+ "1467 20 5174 0 3 \n",
+ "1468 14 13243 3 3 \n",
+ "1469 12 10228 3 3 \n",
+ "\n",
+ " Gender PerformanceRating StandardHours EmployeeCount \n",
+ "0 0 3 80 1 \n",
+ "1 1 4 80 1 \n",
+ "2 1 3 80 1 \n",
+ "3 0 3 80 1 \n",
+ "4 1 3 80 1 \n",
+ "... ... ... ... ... \n",
+ "1465 1 3 80 1 \n",
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+ "1468 1 3 80 1 \n",
+ "1469 1 3 80 1 \n",
+ "\n",
+ "[1470 rows x 32 columns]"
+ ]
+ },
+ "execution_count": 168,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Train=pd.DataFrame(Train,columns=[i for i in t.Features])\n",
+ "Train"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb9399bd",
+ "metadata": {},
+ "source": [
+ "## Classification into Groups"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "id": "35eb39d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OverTime 2 0 1 \n",
+ "JobLevel 5 1 5 \n",
+ "StockOptionLevel 4 0 3 \n",
+ "TotalWorkingYears 40 0 40 \n",
+ "MonthlyIncome 1349 1009 19999 \n",
+ "MaritalStatus 3 0 2 \n",
+ "YearsWithCurrManager 18 0 17 \n",
+ "Department 3 0 2 \n",
+ "JobRole 9 0 8 \n",
+ "EnvironmentSatisfaction 4 1 4 \n",
+ "YearsAtCompany 37 0 40 \n",
+ "JobInvolvement 4 1 4 \n",
+ "Age 43 18 60 \n",
+ "JobSatisfaction 4 1 4 \n",
+ "WorkLifeBalance 4 1 4 \n",
+ "NumCompaniesWorked 10 0 9 \n",
+ "YearsInCurrentRole 19 0 18 \n",
+ "DistanceFromHome 29 1 29 \n",
+ "BusinessTravel 3 0 2 \n",
+ "YearsSinceLastPromotion 16 0 15 \n",
+ "HourlyRate 71 30 100 \n",
+ "RelationshipSatisfaction 4 1 4 \n",
+ "DailyRate 886 102 1499 \n",
+ "EducationField 6 0 5 \n",
+ "PercentSalaryHike 15 11 25 \n",
+ "MonthlyRate 1427 2094 26999 \n",
+ "TrainingTimesLastYear 7 0 6 \n",
+ "Education 5 1 5 \n",
+ "Gender 2 0 1 \n",
+ "PerformanceRating 2 3 4 \n",
+ "StandardHours 1 80 80 \n",
+ "EmployeeCount 1 1 1 \n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in Train.columns:\n",
+ " print(i,len(Train[i].unique()),\" \" ,min(Train[i]),max(Train[i]) ,\"\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "id": "126608d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.cut(Train['MonthlyIncome'], 5)\n",
+ "sorted(df.unique())\n",
+ "Train.loc[(Train['MonthlyIncome'] > 990) & (Train['MonthlyIncome'] <= 4807), 'MonthlyIncome'] = 0\n",
+ "Train.loc[(Train['MonthlyIncome'] > 4807) & (Train['MonthlyIncome'] <= 8605), 'MonthlyIncome'] = 1\n",
+ "Train.loc[(Train['MonthlyIncome'] > 8605) & (Train['MonthlyIncome'] <= 12403), 'MonthlyIncome'] =2\n",
+ "Train.loc[(Train['MonthlyIncome'] > 12403) & (Train['MonthlyIncome'] <= 16201), 'MonthlyIncome'] =3\n",
+ "Train.loc[(Train['MonthlyIncome'] > 16201) & (Train['MonthlyIncome'] <= 19999.0), 'MonthlyIncome'] =4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 171,
+ "id": "e4e760ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.cut(Train['TotalWorkingYears'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ (Train['TotalWorkingYears'] <= 8), 'TotalWorkingYears'] = 0\n",
+ "Train.loc[(Train['TotalWorkingYears'] >8) & (Train['TotalWorkingYears'] <= 16), 'TotalWorkingYears'] = 1\n",
+ "Train.loc[(Train['TotalWorkingYears'] >16) & (Train['TotalWorkingYears'] <= 24), 'TotalWorkingYears'] = 2\n",
+ "Train.loc[(Train['TotalWorkingYears'] >24) & (Train['TotalWorkingYears'] <= 32), 'TotalWorkingYears'] = 3\n",
+ "Train.loc[ Train['TotalWorkingYears'] > 32, 'TotalWorkingYears']=4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "id": "0aad681f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.cut(Train['YearsWithCurrManager'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['YearsWithCurrManager'] <= 1.7, 'YearsWithCurrManager'] = 0\n",
+ "Train.loc[(Train['YearsWithCurrManager'] > 3.4) & (Train['YearsWithCurrManager'] <= 6.8), 'MonthlyIncome'] = 1\n",
+ "Train.loc[(Train['YearsWithCurrManager'] >6.8) & (Train['YearsWithCurrManager'] <= 10.2), 'MonthlyIncome'] = 2\n",
+ "Train.loc[(Train['YearsWithCurrManager'] >10.2) & (Train['YearsWithCurrManager'] <= 13.6), 'MonthlyIncome'] = 3\n",
+ "Train.loc[(Train['YearsWithCurrManager'] >13.6) & (Train['YearsWithCurrManager'] <= 17.0), 'MonthlyIncome'] = 4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "id": "0e129e26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "df=pd.cut(Train['YearsAtCompany'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['YearsAtCompany'] <= 8, 'YearsAtCompany'] = 0\n",
+ "Train.loc[(Train['YearsAtCompany'] > 8) & (Train['YearsAtCompany'] <= 16), 'YearsAtCompany'] = 1\n",
+ "Train.loc[(Train['YearsAtCompany'] >16) & (Train['YearsAtCompany'] <= 24), 'YearsAtCompany'] = 2\n",
+ "Train.loc[(Train['YearsAtCompany'] >24) & (Train['YearsAtCompany'] <= 32), 'YearsAtCompany'] = 3\n",
+ "Train.loc[(Train['YearsAtCompany'] >32) & (Train['YearsAtCompany'] <= 40), 'YearsAtCompany'] = 4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "id": "2bedd67f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# DistanceFromHome 5\n",
+ "df=pd.cut(Train['YearsInCurrentRole'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['YearsInCurrentRole'] <=4 , 'YearsInCurrentRole'] = 0\n",
+ "Train.loc[(Train['YearsInCurrentRole'] > 4) & (Train['YearsInCurrentRole'] <= 8), 'YearsInCurrentRole'] = 1\n",
+ "Train.loc[(Train['YearsInCurrentRole'] > 8) & (Train['YearsInCurrentRole'] <= 12), 'YearsInCurrentRole'] = 2\n",
+ "Train.loc[(Train['YearsInCurrentRole'] >12) & (Train['YearsInCurrentRole'] <= 16), 'YearsInCurrentRole'] = 3\n",
+ "Train.loc[ Train['YearsInCurrentRole'] > 16, 'YearsInCurrentRole']=4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 175,
+ "id": "570a968f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Age 10\n",
+ "df=pd.cut(Train['Age'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['Age'] <=27 , 'Age'] = 0\n",
+ "Train.loc[(Train['Age'] > 27) & (Train['Age'] <= 35), 'Age'] = 1\n",
+ "Train.loc[(Train['Age'] > 35) & (Train['Age'] <= 44), 'Age'] = 2\n",
+ "Train.loc[(Train['Age'] >44) & (Train['Age'] <= 52), 'Age'] = 3\n",
+ "Train.loc[ Train['Age'] > 52, 'Age']=4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 176,
+ "id": "0be404ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Age 10\n",
+ "df=pd.cut(Train['DistanceFromHome'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['DistanceFromHome'] <=7 , 'DistanceFromHome'] = 0\n",
+ "Train.loc[(Train['DistanceFromHome'] > 7) & (Train['DistanceFromHome'] <= 13), 'DistanceFromHome'] = 1\n",
+ "Train.loc[(Train['DistanceFromHome'] > 13) & (Train['DistanceFromHome'] <= 18), 'DistanceFromHome'] = 2\n",
+ "Train.loc[(Train['DistanceFromHome'] >18) & (Train['DistanceFromHome'] <= 24), 'DistanceFromHome'] = 3\n",
+ "Train.loc[ Train['DistanceFromHome'] > 24, 'DistanceFromHome']=4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 177,
+ "id": "18762dcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "df=pd.cut(Train['HourlyRate'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['HourlyRate'] <=44 , 'HourlyRate'] = 0\n",
+ "Train.loc[(Train['HourlyRate'] > 44) & (Train['HourlyRate'] <= 58), 'HourlyRate'] = 1\n",
+ "Train.loc[(Train['HourlyRate'] > 58) & (Train['HourlyRate'] <= 72), 'HourlyRate'] = 2\n",
+ "Train.loc[(Train['HourlyRate'] >72) & (Train['HourlyRate'] <= 86), 'HourlyRate'] = 3\n",
+ "Train.loc[ Train['HourlyRate'] > 86, 'HourlyRate']=4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 178,
+ "id": "6a2d733a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# DailyRate\n",
+ "df=pd.cut(Train['DailyRate'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['DailyRate'] <=380 , 'DailyRate'] = 0\n",
+ "Train.loc[(Train['DailyRate'] > 380) & (Train['DailyRate'] <= 660), 'DailyRate'] = 1\n",
+ "Train.loc[(Train['DailyRate'] > 660) & (Train['DailyRate'] <= 940), 'DailyRate'] = 2\n",
+ "Train.loc[(Train['DailyRate'] >940) & (Train['DailyRate'] <= 1220), 'DailyRate'] = 3\n",
+ "Train.loc[ Train['DailyRate'] > 1220, 'DailyRate']=4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 179,
+ "id": "8ec59f1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# MonthlyRate\n",
+ "df=pd.cut(Train['MonthlyRate'], 5)\n",
+ "sorted(df.unique())\n",
+ "\n",
+ "Train.loc[ Train['MonthlyRate'] <=7075 , 'MonthlyRate'] = 0\n",
+ "Train.loc[(Train['MonthlyRate'] > 7075) & (Train['MonthlyRate'] <= 12056), 'MonthlyRate'] = 1\n",
+ "Train.loc[(Train['MonthlyRate'] > 12056) & (Train['MonthlyRate'] <= 17037), 'MonthlyRate'] = 2\n",
+ "Train.loc[(Train['MonthlyRate'] > 17037) & (Train['MonthlyRate'] <= 22018), 'MonthlyRate'] = 3\n",
+ "Train.loc[ Train['MonthlyRate'] > 22018, 'MonthlyRate']=4\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 180,
+ "id": "6e35f71f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OverTime 2 0 1 \n",
+ "JobLevel 5 1 5 \n",
+ "StockOptionLevel 4 0 3 \n",
+ "TotalWorkingYears 5 0 4 \n",
+ "MonthlyIncome 5 0 4 \n",
+ "MaritalStatus 3 0 2 \n",
+ "YearsWithCurrManager 17 0 17 \n",
+ "Department 3 0 2 \n",
+ "JobRole 9 0 8 \n",
+ "EnvironmentSatisfaction 4 1 4 \n",
+ "YearsAtCompany 5 0 4 \n",
+ "JobInvolvement 4 1 4 \n",
+ "Age 5 0 4 \n",
+ "JobSatisfaction 4 1 4 \n",
+ "WorkLifeBalance 4 1 4 \n",
+ "NumCompaniesWorked 10 0 9 \n",
+ "YearsInCurrentRole 5 0 4 \n",
+ "DistanceFromHome 5 0 4 \n",
+ "BusinessTravel 3 0 2 \n",
+ "YearsSinceLastPromotion 16 0 15 \n",
+ "HourlyRate 5 0 4 \n",
+ "RelationshipSatisfaction 4 1 4 \n",
+ "DailyRate 5 0 4 \n",
+ "EducationField 6 0 5 \n",
+ "PercentSalaryHike 15 11 25 \n",
+ "MonthlyRate 5 0 4 \n",
+ "TrainingTimesLastYear 7 0 6 \n",
+ "Education 5 1 5 \n",
+ "Gender 2 0 1 \n",
+ "PerformanceRating 2 3 4 \n",
+ "StandardHours 1 80 80 \n",
+ "EmployeeCount 1 1 1 \n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in Train.columns:\n",
+ " print(i,len(Train[i].unique()),\" \" ,min(Train[i]),max(Train[i]) ,\"\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 181,
+ "id": "f6854e96",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Train1=Train\n",
+ "Train1[\"Output\"]=Y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80e30f22",
+ "metadata": {},
+ "source": [
+ "# EDA with Features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 182,
+ "id": "6c9af473",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 182,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"OverTime\",hue='Output', data=Train1)\n",
+ "\n",
+ "# OverTime\n",
+ "# 1 NO\n",
+ "# 2 Yes\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 183,
+ "id": "1422c93b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 183,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"JobLevel\",hue='Output', data=Train1)\n",
+ "\n",
+ "# JobInvolvement\n",
+ "# 1 'Low'\n",
+ "# 2 'Medium'\n",
+ "# 3 'High'\n",
+ "# 4 'Very High'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "id": "de3d19ee",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 184,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"StockOptionLevel\",hue='Output', data=Train1)\n",
+ "\n",
+ "# StockOptionLevel\n",
+ "# 1 'No'\n",
+ "# 2 'Low'\n",
+ "# 3 'Medium'\n",
+ "# 4 'High'\n",
+ "\n",
+ "# Mostly New Freshers (switching jobs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 185,
+ "id": "49207823",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 185,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"MonthlyIncome\",hue='Output', data=Train1)\n",
+ "\n",
+ "# df=pd.cut(df['MonthlyIncome'], 5)\n",
+ "# sorted(df.unique())\n",
+ "\n",
+ "# MonthlyIncome\n",
+ "# 1 Interval(990.01, 4807.0, closed='right')\n",
+ "# 2 Interval(4807.0, 8605.0, closed='right')\n",
+ "# 3 Interval(8605.0, 12403.0, closed='right')\n",
+ "# 4 Interval(12403.0, 16201.0, closed='right')\n",
+ "# 5 Interval(16201.0, 19999.0, closed='right')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 186,
+ "id": "d95bb138",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 186,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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oixcvKilxryJq1lL16jXUoGGcAgMrK3lfkiN3AQAAXKdErLF4/u9P66n+8apStaraPNBWmWaz3N3dJUleXl6SpEyzWebr2iXJ08tLZrM53zGtVquysrLsfxZLVtHvCAAAtzmHrrG45p8vjtbJE7/o3bff1No1q/PtYzLl/90btS9dslCLFswzqEIAAFAQDg0WKSlHde7sWcXWraeKFSspOiZGiXv3yMfHVxcuXJAkWSwWSZKPr598fHyUdua0/fsWi0W+fv75jt2pS1e179Dpur5ZGjZoQNHtDAAAcOypkGNHj+jtNybpwP5k/fzzMR3Yn6xqd9yhqOjaWr82QWdOn9aahFWKiIiUm5uboqJjtD95nw7sT9aunTt0KjVVUVHR+Y7t6uoqT09P+5+Hh2cx7x0AALcfh85YNGvRSikpRzVl0gTZbDbddU9TPfBgezVufLdmznhXzz07XEHBwRo8dIQkqX6DhmrbroOmTJogZxdnxffrr+CQUEfuAgAAuI5Dg4XJZFKf+MfVJ/7xXO2VAgI0etz4fPv37BOvnn3ii6tEAABwC0rEVSEAAKBsIFgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADBMoYLF00MH3vB22f/58F96ecxLf6koAABQOt3SfSySEvdKkk6f/lXHUo7Y31+Tk3NZiXt+0pkzZ4yrEAAAlBq3FCzGjxslySTJpC2bN2vL5s359LIpunaMIcUBAIDS5ZaCRcO4RpJM2rZ1swICAxUWVi3X587OzqpStaraPfSwgSUCAIDS4paCxd+ff1GS1Kt7F9VvEKd+/Z8skqIAAEDpVKhnhUyd8S+eFgoAAPIoVLCoUKGiVq/8TkmJe2U2n5PNlvtzk0kaNTbvQ8QAAEDZVqhgMfu/H2nF8mWSbDfoYSp8RQAAoNQqVLDY+OMGeXl7acCTg1W5ShWZTAQJAABQyGBhvWRVk7vuVpO77zG6HgAAUIoV6s6b9Ro01Jkzp42uBQAAlHKFmrGoERGhzz6ZrQ/en6Hg4JB8+zz0cKe/VBgAACh9ChUs5nw8S5K0ZvVK5V2oaZNkIlgAAHAbKlSweKRr96vXlAIAAFynUMGia/eeRtcBAADKgEIFixs9Mv0ak0l6pOvfClUQAAAovQoZLD7X1bUVf7xB1u+nRwgWAADcfgoVLB7r2z/f9hO//KJVK5dfXYMBAABuO4UKFjd7LPrPx1KUvG9foQsCAAClV6FukHUzlQIDdWA/wQIAgNtRoWYsjhw+lKfNZrMpNfWkdm7fKi9v779cGAAAKH0KFSxeGvmcbvwEU5seaNu+8BUBAIBSq1DBolZUdJ4nmppMJvn5+atu/QZq3qKVEbUBAIBSplDBYszLE4yuAwAAlAGFChbXHDp0UPv3JSk7O1t+/v6KiYlVYOXKRtUGAABKmUIFi5ycHM2Y9o42/rDht5arDx4zmaQH2z2k+H5PGFchAAAoNQoVLJYsXqCNP3wvPz9/3d20mfzLl1dGero2/rBBy775WpUCAtT+oY5G1woAAEq4QgWL9WsSVLFiJU2c8ra8r7u09NFuPfTC889q1YrvCBYAANyGCnWDrIyMdEXXjskVKiTJ29tb0dExOv3rr4YUBwAASpdCBYsKFSrq0MEDslqtudovW606dOig/Pz8DCkOAACULoU6FdKsZSstXjBPo1/4h5q3vFflK1TQubNntX7dGp345Rd17NzF6DoBAEApUKhg0eWRbvo5JUVbNm/Up598fN0nNtWr31Bdu/UwqDwAAFCaFCpYODs769nn/ql9SXu1ZdMmZWdb5OnlpSpVg3RfmweMrhEAAJQShQoWFy9e1MzpU7V500a9OHqcYurEKjs7W/3je2rn9q0a/sxzcnNzM7pWAABQwhVq8ebiBfO0edOPCgoKkr9/eUmSi7Ozqt1xp7Zt3aolC+cbWiQAACgdChUsNm7coNCwcL3+5lSFhIZKklxcXTVh0huqVq2avl+/1tAiAQBA6VC4+1ikZ+iOO+6Uk7NzrnaTyaSw8Go6d+6sEbUBAIBSplDBIjAwUHt+2q3zmZm52jMzzdrz024FVq5iSHEAAKB0KdTizfsfbKePZ32oZ4YPUp3YevLx9VWm2ayfdu9SVlaW+vUfYHSdAACgFChUsHiw3UM6d+6svvrfF9q08YffB3NxUedHHtUDbdsbViAAACg9ChUsJKl7j95q99DDOrB/vy5cOC9vb29FRETK28fHyPoAAEApUuhgIUk+Pr5q0DDOqFoAAEApV6jFmwAAAPkhWAAAAMMQLAAAgGEIFgAAwDB/afGmEb5cukTffPU/XbyYrdh69TVoyAidz8zUzBlTdfjQQVUNCtbAIcMVHl5NNptNcz+drbUJq+Xs7KwOHTurfYeOjt4FAADwG4fOWOzetVPz5n6qIcOf0csTJis5KUnLvvlScz6ZpXLl3DXlrWkKCwvXBzOnS5J27timld8t0/MvjtaAgUM0Z/YsHUs56shdAAAA13FosHBxcVGvPvGqE1tXoaFhqhoUpHNnzykpca+atWipSgEBatW6jQ4fOqiLFy8qKXGvImrWUvXqNdSgYZwCAysreV9SvmNbrVZlZWXZ/yyWrGLeOwAAbj8OPRUSXTtG0bVjJEkpR4/o4IH96tWnr5Yv+1ru7u6SJC8vL0lSptkss9lsb5ckTy8vmc3mfMdeumShFi2YV8R7AAAArufwNRaSlJZ2RlMmTdBDHTqpRkTNfPuYTPl/90btnbp0VfsOnezvLZYsDRvEM0wAAChKDg8WZrNZr40fp9oxddS9Z29JV+/oeeHCBUmSxWK52ubrJx8fH6WdOW3/rsVika+ff77jurq6ytXVtWiLBwAAuTh0jUW2xaLXXxuvSpUqqU/fx5WVdUFZWVmKiq6t9WsTdOb0aa1JWKWIiEi5ubkpKjpG+5P36cD+ZO3auUOnUlMVFRXtyF0AAADXceiMxaZNP+rQoQOSpKf6x0uSKgUEaOzLr2nmjHf13LPDFRQcrMFDR0iS6jdoqLbtOmjKpAlydnFWfL/+Cg4JdVj9AAAgN4cGi5atWqtlq9b5fjZ63Pg8bSaTST37xKtnn/iiLg0AABQCd94EAACGIVgAAADDOPyqEACOET5hp6NLyCPlpXqOLgHAX8SMBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAxDsAAAAIYhWAAAAMMQLAAAgGEIFgAAwDAuji4ABXN8TAtHl5BHyCvrHF0CAKCEYcYCAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAxDsAAAAIYhWAAAAMMQLAAAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMAQLAABgGIIFAAAwjIujCzh37qw2fL9O69YkaOjwZxQaFq4zp09r5oypOnzooKoGBWvgkOEKD68mm82muZ/O1tqE1XJ2dlaHjp3VvkNHR+8CAAD4jUNnLCwWi4YPfkobN2xQytEj9vY5n8xSuXLumvLWNIWFheuDmdMlSTt3bNPK75bp+RdHa8DAIZoze5aOpRx1UPUAAOCPHBos3NzcNG3mBxr+zP/lak9K3KtmLVqqUkCAWrVuo8OHDurixYtKStyriJq1VL16DTVoGKfAwMpK3pfkoOoBAMAfOfRUiLOzs/z8/HX611O52jPNZrm7u0uSvLy87G3m69olydPLS2azOd+xrVarrFar/b3FkmV0+QAA4A8cvsaioEymW2tfumShFi2YV3QFAQCAPEpksPDx8dWFCxckXV2HIUk+vn7y8fFR2pnT9n4Wi0W+fv75jtGpS1e179Dpur5ZGjZoQNEVDQAASmawiIqurfVrExQVVVtrElYpIiJSbm5uioqO0XfLvtGB/cnKysrSqdRURUVF5zuGq6urXF1di7lyAABubyUyWPSJf1wzZ7yr554drqDgYA0eOkKSVL9BQ7Vt10FTJk2Qs4uz4vv1V3BIqIOrBQAA15SIYBEQWFlzF3xhf18pIECjx43P089kMqlnn3j17BNfjNUBAICC4s6bAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDlIj7WADA7SB8wk5Hl5BHykv1HF0CyhhmLAAAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYbjzJnADx8e0cHQJeYS8ss7RJQDATTFjAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAxDsAAAAIYhWAAAAMMQLAAAgGEIFgAAwDAECwAAYBgXRxcAAEBRCZ+w09El5JHyUj1Hl1CkmLEAAACGIVgAAADDcCoEQIlxfEwLR5eQR8gr6xxdAlCqMGMBAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADBMqbyPxY8bvtfnn32iCxfOK67xXXriyUFydXV1dFkAANz2Sl2wyMw06/333tXjAwaqRo2amjB+jBJWrdADbds7ujQAKHW4KVnxK+vHvNSdCjl08IBsNqllq9YKCQ1V/foNlZS419FlAQAAlcIZC7PZrHLu5WQymSRJnl5eOnXqVJ5+VqtVVqvV/j4r64IkyWLJ+tNtOOdkG1StcbJzHF1BXllZf34sC4pjXjAc8+LHMS9+HPPi92fH/Npvp81m+9OxTLaC9CpB1q1N0Ccf/0f//s8nkqQ5s2fp6JHDGjV2fK5+C+fP1aIF8xxRIgAAZdL09z9UxYqVbtqn1M1Y+Pj4KNti0ZUrV+Tk5CRLVpZ8/fzz9OvUpavad+hkf3/lyhVdOH9e3j4+9tmO0sJiydKwQQM0/f0P5eHh6ehybgsc8+LHMS9+HPPiV1qPuc1mU3a2ReXLV/jTvqUuWFSvUVNOTk5atWK5omPqaOfO7eryaPc8/VxdXfNcKeLt7V1cZRYJDw9PeXqWnn+IZQHHvPhxzIsfx7z4lcZj7uXlVaB+pS5Y+Pr6atCQEZr76WzNmztHjRrfpZatWju6LAAAoFIYLCTp7qbNdHfTZo4uAwAA/EGpu9z0duTq6qpHu/2Nm4AVI4558eOYFz+OefG7HY55qbsqBAAAlFzMWAAAAMMQLAAAgGEIFgAAwDCl8qqQ2wlPcnWMc+fOasP367RuTYKGDn9GoWHhji6pTPty6RJ989X/dPFitmLr1degISPk7u7u6LLKrJycHM2bO0erV34nSarfIE5PDhoqNzc3B1d2e3jnzde1aeMPmrvgC0eXUiSYsSjBrj3JtUvX7ho3fpJ27dyuhFUrHF1WmWexWDR88FPauGGDUo4ecXQ5Zd7uXTs1b+6nGjL8Gb08YbKSk5K07JsvHV1Wmfb9+rVavXKF/jFylF6eMFl7ftptDxkoWlu3bNaWzRsdXUaRIliUYDzJ1THc3Nw0beYHGv7M/zm6lNuCi4uLevWJV53YugoNDVPVoCCdO3vO0WWVaS1btdaH/52jyFpR8vf3l4uLi5ydnR1dVpmXlZWlWR/9Sw+2e8jRpRQpgkUJlt+TXM1ms4OrKvucnZ3ll8/zZ1A0omvHqH2HjpKklKNHdPDAfjVt3sLBVd0env/703qqf7yqVK2qNg+0dXQ5Zd7cObNVJ7aeYurEOrqUIkWwKGVK2fPTgAJLSzujKZMm6KEOnVQjoqajy7kt/PPF0Xph1FilHD2qtWtWO7qcMm1fUqK2btmkPo/1c3QpRY5gUYJd/yRXSTd8kitQ2pnNZr02fpxqx9RR9569HV1OmZeSclS7d+1UxYqVFFOnrqJjYpS4d4+jyyrTlixaoMzMTD09bKDefftNSdITfXs5uKqiwVUhJVhBn+QKlGbZFotef228KlWqpD59H1dW1gWZTE6l7smPpcmxo0f0nw//pRdHvyx3Dw8d2J+shx7u5OiyyrTBQ0fIar0kSdqz5yd9MHO6Jk1528FVFQ2CRQnGk1xxO9i06UcdOnRAkvRU/3hJUqWAAE1779+OLKtMa9ailVJSjmrKpAmy2Wy6656meuDB9o4uq0zzL1/e/trP75gkKSCwsqPKKVI8KwQAABiGNRYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAxDsAAAAIbhPhZAMZs5farWrU24aZ+o6Noa8/KEYqnjmf97Xg3jGql/3166fDlH//n4U7l7eEiSzmZkaPBTj6tCxYqa8f5H9u/u2rlDkya8rOjadTR63Pi/VMfC+XO1aME89X6snzp07Jxvn6lvTVFS4l793z9GqmZkrb+0vetNfHWcdu/aqfYdOuqxvv1zffbt119q9n8/UmhYuCZNeVtOTvz/MKAgCBZAMYuJrSt3dw/7+++WfyNJuW5QVKVq1QKPN/K5Z5SSclTvzvhXoW+44+LqqjvurK79yft09Ohh1YqqLUnau/cnSVJ6WppOpZ5U5SpX6zp86KAkGfojfzNNm7dQcGioqlQp+HEpiL/17KOfdu/SqhXL1anzo/L185MkXbZa9dX/vpAkde/Rm1AB3AKCBVDMmrdopeYtWtnfXwsWjw94ykEVXVWzZi3tT96nw4cO2YNF0nXPj0jcuydPsIisVfhgYbPZVND788U1aqK4Rk0Kva0bubN6DTVqfJc2b/pRX3/1P/Xs/ZgkKSFhldLT01QjoqbiGjU2fLvXy8nJ4ZHlKFMIFkAJtmvnDi1ZNF9HjxyRs7OTYurEqmfvvqpStaoS9/6k8eNG2/uOGDow1ymUdWsTtOybr3T855/l5e2l2Nh66tmnr/z9/fPdVs1ataQvfw8N0tUwERhYWWlpaUrcu0f33ne/JOnw4YMymUyqERFZoFolaW3CKr3/3jQ1bd5SGelpOrA/WSNfGpOnjsxMs8a8+E+lpp7UY/36q/1DHfXK2JeUlLhXr056Q9Wr17Cfxhk0dIR+3PC9khL3yNvbR/fe10Zdu/e0j3Xil18066MPlLwvSZ6enmrd5n7t2L5NR48c1twFX0iSuvfsra1bNmnF8m/0cKcucnd31/++WCTp6ozG9X7avUsL58/V0SOH5e7uoUaNm6h3/OPy+O3U0aVLl7R08UJt+H6d0tPTVL5CBTVvea86d+kqF5er/7nt2a2zypevoHuaNdf6tWtUr34DDR72tDIy0vX5p59o966dys62KCQ0TF0e7aYGDRv9+T8UoARhfg8oobZu2azJr41XytEjahDXSKFh4dq8aaPGvPRPnTl9WuUrVNQDD7aXj4+vJKl5y3vVqPFdkqTl336tmdOn6mxGhpo2a67KgVW0bm2C3n5j0g23d+20xrVgkZGRrpMnT6h+wzhVr1FDiYlXZy/Onj2r9LQ0BQWHyNvbu0C1Xm/D+rW6dOmSmrdoJT//8rk+s1qtenPyRKWmnlTbdh3U/qGONz1G/35/hpydndS4yd06fz5TixbM0/ZtWyVJly5e1ITxY7Tnp10KDQtT7Zg6WrXiOx09cjjXGMHBIWreopUsFou+/fpLrV+3RmdOn1ZMnVjF1Im199u+bYsmvvqyTpz4RXfd3VRBwSFatfI7zZw+1d5nxrtva/Gi+fLw9FSz5i1lkkmL5n+uRfM/z7XNjIx0rV+3RvUbxqlWdG3ZbDZNfPVlrVuboOCQEMU1aqKfj6XojckT9dPuXTc9BkBJw4wFUELN+fg/stmu6J8vjlGtqGhJ0gfvz1DCqhVa+sUiPfHkID0+4Ckl70tUZqZZ3br3sK+xsGRb1KLlverUpauCgoNls9n096eHan/yPqWnpalCxYp5tufn56/KlasoNfWksrKylLjn6vqKmDqx8vT01JJFC3Qq9aR++eW4JCnyuvUVBan1mti69TXypTEymUy5tm+z2fTetHeUnJykuEaN9Vi/3Isp89Pl0e56tNvfJElhS6vpszkfKylxjxo0jFPC6pVKT0tT3XoN9PwLo+Tk5KTTv57S358ZJqvVmmucR7v30Ibv12n5t1/J67ew9MfZitmzPpKTk5NemTBZVasGSZKmTHpVWzZv1M/HUhQcEioPT0/d1+YB9XviKbm4uCg9LU1DBz2hjT9u0N96/T6es7OzJkx8Q5UCAiRJp06l6udjKQoJDdOosVcXw25Yv1ZfLFmk/cn7VCe27p8eC6CkIFgAJdDJkyd06lSqQkLD7D/UkvTAg+2UsGqF9ifvu+n3O3fpqgsXzmvzxh+1ft0aZWVdUHZ2tiTp7NmMfIOFJNWsFaVTp1J15PAhJe7dIycnJ0VHx8jd3UNLFi1Q4t49Sks7c7Xvb8HiVmutUqVqnlAhSatWLNepU6kKDg7RsKf/XqAFkyEhobnGlaRMs1mSdOi3mZd7W7exjxUQWFmurq55gkVAQKDa3N9Wy779ShcuXFBco8aqEVHT/nnqyZM6dSpV3t4+Wvb1V/b2s2fPSpKOHUtRaFi4Bg0ZrlOpJ7Xyu2VKSzujSxcv/dYvI9f2XFxc7aFCksqXr6Dy5Svo5IkTWjh/rho2aqJ7mrVQ0+Yt//QYACUNwQIogcznzkmSypfPfarAv3wFSVJ2tuWm39+xfaveffsNe5i43s2WS9aMrKX1axN0+NBBJe7dozvurC5PLy/VjKwlV1c3Je7dowsXLtj7GlHrNadOpUqSTp/+VWfOnFZwcEiBvmf3W1a5tiDUXleFCgX6eudHu2rZt1dDQ6cuXXN9di0YnD+faV9sm+vzjKufL5w/V4sXLpDNdiXX53+2RtXNzU2jX35Vn33ysZYuWaRFC+bJ189Pbe5vqy6PdrOvzwBKA/61AiXQtbUL5377cbwmIyNd0tXTFjcze9ZHys7OVny/J9Si1b3y8vK2L4C8mZo1ry7G3LZ1s1JTT6pTl0clXf3hi6gZqaTEvbp8+bJ8fHxVNSjYkFqvqRQQoLvvaaYvly7Re9Pe0SsTJv+lqyWuBZ1zv80q/Jnr6/T77bLTazw8PSVJQUHBenPqjHy/n5Z2RosXzpeHh4ee/r/nVatWlNzKlVPPbp0LtP2qVYP09+df0MWLF5W8L1FfLl2ixQvnyWa7ou49ehdoDKAkYPEmUAIFBYeoQsWKOpZyVAcP7Le3r1qxXJJUp87v59ydfvvxvZyTY2/LyMiQyeSk+9o8IC8vb1mt1jw//PkJCQ2Tp6enkvclSVKuxYu1Y2KUlnZG586dzXX/ilup9WYebPuQevaOV1R0bR0+dFBLFi8o0Pdu5M4aEZKkNQkr7bMY6enpeU6DFERoSKh8/fx04sQv2rpls7397NmzWr1qhf21zWZTcHCoYuvWk1u5cnkWrt7IqhXL1a9PD30860OVK1dOsXXr20NdytGjt1wv4EjMWAAlkMlkUp/HHte0qW/qtfFjVb9BnDIy0pWUuFeVKgWo/cO/Xy1RpWqQjhw+pGnvvKn69RuqW49eqluvnjZv2qixo19QaFi4khL32H/krJcu3XC7Tk5OqhERqd27dsjV1U01I6Psn9WOidWCeXMl5b4x1q3UWpD9HjRkhP753NP6YtECNWgQpzur1yjw96/X6t779L8vFmn7tq0a/eLzCgkN087t2woVLJycndUn/nG9N+0dvf3GJDVoGCcPD0/t2L5NVusl1avfUKGhYapQoaIOHEjW5NfGq1y5cvpp905J0uXLVtlstnzXlkhSXOMmmj/vMy3/9hudSk2Vn5+fdu7cLkmq3zCuUPsPOAozFkAJdXfTZvq/f7ygoKAQbdm8Ucd//lnNmrfUyxMmy8vL296va7ceuuPO6vrl+M9KTT0pSRowcIiat2ilM2dO66ddO9UwrrGat7xXkuxXddzItZte1YyMlJubm729eo0Iubu72z8rTK0FEVi5snrHP66cnBy9N+0dXbpJELoZV1dXvThqnOrE1tWxlBQl7d2jhx7upHLlyhXqFEvzFq30j5GjVCOipnbv2qmtWzareo0IjX3lNVWoUEFubm56/sXRiq5dR/uS9irl6BH16PWYgoJDlJOTY//fJj9+fv4aN36iGje5S/uT9+mHDevl4+OrJ54arDb3P1io/QccxWQr6K3vAKCU2Z+8T2Hh1eyB6NDBAxr1wj8UGhqm199618HVAWUTp0IAlElms1mvjR8rT08v1YquLdls9ptndena3cHVAWUXMxYAyqzkfUlasmiB9icnydnZRaFhYXqka3fFFHBBKYBbR7AAAACGYfEmAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADDM/wMndV/4X4CCBwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"TotalWorkingYears\",hue='Output', data=Train1)\n",
+ "\n",
+ "# 0 (990.01, 4807.0]\n",
+ "# 1 (4807.0, 8605.0]\n",
+ "# 2 (8605.0, 12403.0]\n",
+ "# 3 (12403.0, 16201.0]\n",
+ "# 4 (16201.0, 19999.0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 187,
+ "id": "9a2b6ceb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 187,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"MaritalStatus\",hue='Output', data=Train1)\n",
+ "# Mostly Married employees Stays\n",
+ "# 0 Married \n",
+ "# 1 Single \n",
+ "# 2 Other "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 188,
+ "id": "b673cc93",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 188,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# YearsWithCurrManager\n",
+ "sns.countplot(x=\"YearsWithCurrManager\",hue='Output', data=Train1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 189,
+ "id": "154ccdb3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 189,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"JobRole\",hue='Output', data=Train1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 190,
+ "id": "3b3e832f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 190,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"JobInvolvement\",hue='Output', data=Train1)\n",
+ "\n",
+ "# JobInvolvement\n",
+ "# 1 'Low'\n",
+ "# 2 'Medium'\n",
+ "# 3 'High'\n",
+ "# 4 'Very High'\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 191,
+ "id": "f7323353",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 191,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"JobSatisfaction\",hue='Output', data=Train1)\n",
+ "\n",
+ "# JobSatisfaction\n",
+ "# 1 'Low'\n",
+ "# 2 'Medium'\n",
+ "# 3 'High'\n",
+ "# 4 'Very High'\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 192,
+ "id": "4c9ac00d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 192,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"Department\",hue='Output', data=Train1)\n",
+ "\n",
+ "# 0 Research & Development\n",
+ "# 1 Sales\n",
+ "# 2 Other"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "id": "4653b0b7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 193,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"NumCompaniesWorked\",hue='Output', data=Train1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "id": "679a7714",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 194,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"YearsInCurrentRole\",hue='Output', data=Train1)\n",
+ "\n",
+ "# 0 0-4 yrs\n",
+ "# 1 4-8 yrs\n",
+ "# 2 8-12 yrs\n",
+ "# 3 12-16 yrs\n",
+ "# 4 16-20 yrs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "id": "8284bfc6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 195,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"Age\",hue='Output', data=Train1)\n",
+ "\n",
+ "# 0 18-27\n",
+ "# 1 27-35\n",
+ "# 2 35-44\n",
+ "# 3 44-52\n",
+ "# 4 52-60"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "id": "e984bfd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Train2=Train1.iloc[:,:6]\n",
+ "Train2[\"Output\"]=Y\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 197,
+ "id": "88767296",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 197,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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ypltKfG92c8PLy6tSAzxl5oxviYvdyguTpwAQGBDILbfeQYOGjcjLy2PqG69So1Yt+g8YfM7xf5w9k1kzvrsosYnIxaX8FTEu5a+IsSmHRYxL+StibMphEXFV5Soo+Pj6AnDdsBto2LCR/fPFNu/3X/lt7k88+ezzhFapChQXLzp16UZQUBAAkdHRHNi/v9RpDBoylL79B9k/W63Z3DfGsScpRMS5lL8ixqX8FTE25bCIcSl/RYxNOSwirqpCL2UeOmwE+fn57Ni+jfS0NGzY7N/l5+Vx+PChEl0VlVdRYSFmNzcAli9bwldffMaDD4+nWrVqZGWdwNPDk7jYrbw+5SUmPP0cfv7+7Ni2jb4DBpY6TYvFgsViqXBMIuI8yl8R41L+ihibcljEuJS/IsamHBYRV1WhgkJSYgKTX5xEUmJiqW0upKDw2pTJREZF02/AIL775ivy8/OYMvl5+/djxo6jR68rGTBwCG++9gqFhQW079CJPn36VXieIiIiIiIiIiIiIhWVkpxMZmZGhcf39w+gStWqlRiRSOWrUEHhm68+JykxgejmLTh+7BhHjhyhe4+eHDuWQuzWLQwYdF25ple1WnWmz5hj//zIYxPsf0+d9kGp490wchQ3jBxV7vhFREREREREREREKktKcjIPPXgf+Xm5FZ6GxcOT1998W0UFcWkVKihsi4+nZasYHn3iaT79+AOOHDnCmHvvp6ioiPEPP0Bq6vHKjlNERERERERERETEJWVmZpCfl0t477F4hNQs9/h5xw+T8Mc0MjMzVFAQl1ahgkJubi6BQcEA+AcEAHAiMxM/f38aNmrMhnVrKy9CEREREREREREREQPwCKmJV7V6zg5D5KIxV2SkuvXqsW7tanZs30Z4WDhgY+7Pc0hIOMKObfGY3So0WRERERERERERERERcVEVuvI/aMj1ZJ3IYsH8P2nXoRP+/gH8NGc2Dz9wH4mJiXTq3LWy4xQRERERERERERERESeqUJdHMa3b8vD4x0hPT8fDw4NHn3iK6V9/SXpaKi1axTB8xE2VHaeIiIiIiIiIiIiIiDhRhQoKAG3atrf/Xb9BQyY8PbFSAhIREREREREREREREddToS6PHrj3HmbN+O6c333y0ftMfHrCBQUlIiIiIiIiIiIiIiKupVxPKMTHxQKQnHyUA/v32j+fUlhYQNzWLaSkpFRehCIiIiIiIiIiIiIi4nTlKihMevZJwASYWLN6NWtWrz5HKxuRUdGVEpyIiIiIiIiIiIiIiLiGchUU2rRtB5hYt3Y1VatVo3btuiW+d3NzIyw8nGv7DajEEEVERERERERERERExNnKVVB4ePwTAIwcPoSY1m259fa7LkpQIiIiIiIiIiIiIiLiWspVUDjlldemEhQcXGJYZmYGnp5eeHh4VEpgIiIiIiIiIiIiIiLiOspVUFi+bAlff/EZXbv3YOSo0QAcOniQt/73GgcPHMDd3Y2re1/LzaNvvyjBioiIiIiIiIiIiIiIc5gdbbh1yybemfoGaWmpeHh6ApCXl8eUyc9z8MB+vLy8sNls/PbLXJYuXnjRAhYRERERERERERERkUvP4YLCzz/OwWKxMOnFVxg6bAQAixb+RXLyUdp37MRHn33Fm2+9i6+fL3/O+/2iBSwiIiIiIiIiIiIiIpeewwWF3bt2EtO6LQ0aNrIPW7Z4MWDixptuwWw2E1qlKq1atSYh4cjFiFVERERERERERERERJzE4XcoFBUV4uPjY/98NCmJXbt20KBhI6pXD7MPd3NzIz8v3+EA0tPTWL5sCUsWLeTecQ8SUbtOie9PnDjB+9PeInbrZoJDQrn9znuIim4OwK9zf2LuT3MoLCyke89ejBw1GpPJ5PC8RURERERERERERETEMQ4/oVCtehjb4uMoKiwE4OcfZwMmOnfpZm9js9nYuWM7YWFhpUylJKvVyrj/3M3K5cvZv2/vOdv8OHsmx46lMHnKm7Tv0Il3pr5BQX4+Bw8e4KsvPmPMveN45LEJzPv9VzasX+fo4jhk0+Fc1h3Oq9Rpiri6Q8dSWXswi11H050dygU5ePwY8QdT2J90zNmhVEhsYg6rD+U6O4wKW3Eghy1JVmeHUSE7Uk6w8mAO+45mOjuUCtmYkMuGBOOuO6dsTrCyKcGY69CF2puayoqDOcQnpDo7FKfYlJDL+stgHa6IQ6mpbD94lEOpxv6/35mYxuqDVvYadDm2JmSz4Ygxtz+pqamsPJDD1sRsZ4dSIduTMll90EriiQxnh1IhWxKy2ZJgzN/+dJsOW9ly2JjLsT+5eB+6LcWY69DWI9msOZzj7DAqbHVCHluSHb/B1JXsTM1hxaE8dh035vZfRORScrig0L1nLxITE3hs/ENMfuE55v/1B/7+/vS64iqguEukt//3OgkJCXTs3MWhaXp4ePDWux8w7sGHSm0THxdLh46dqVa9Oldd05vU1OMkJSWyLS6W8PBwWrSMoWGjxjRu0pRt8bGOLk6ZPt2cw+CfrfSfncW767VDkX+HlXszePRvd66cmcdDS0zM32XMA/HNexJ4bKmFjjPd+O8yd9btNFY3bF9syWH43Bz6/pDNlFXG2v6kp6fz3LJs+s22cutveXwTa6z4f96ezbj5RfSeaeX5dSZWHspydkjl8smmHAbOttL/h2w+2Gis3/50H23Mod/sXPrNzuFDAy9HRfy528r4xe70mWnlyZXu/LHz37X8H23Mof9sKwNnZ//r/u837z7MxIXZtJ9pYeLCbDbvOezskCpkwc5Mxv/txtUzc3hsiTuLdxqrOPvFZitDfs6j96wcpq411jq472gqr2725NofrNw5L59vDbYPnrMti//Mt3HNrBwm/e3G+sPGOg79YKOVPj/k0m92Hp9sMtZvf7r31lu5dk4OA37K49PNxrqwvXRvFs+uttBnppUH58NP2431//BNrJWbf88vPg5dnk2qwYqyL6+ycu33Wdz4UzZfbTXWuvPrnlwenp9HnxlZPL2skIX7jVkUERG5VBwuKFxzzbVERbfg0MEDbN60AQ8PT+69/0G8vL0BmPPDTFb8vYyI2rXp22+gQ9N0c3MjMDDovG0yMzLw8vICwNfHF4CMjAwyMjLwPDkcwMfHl4yM0g868/Pzyc7Otv+zWku/4yI3N5f3N+ayL72IxCwbz6/IuSzu9hQpy9JEM9/E5ZORZ+Pn3QUsPuLwJuKiKk/+AixKtPDNtgIy8mzM3lXIXwmelyjSyjF3dz5xx4o4ZrUxYYmVP/cYZ/szP9GTZ5fnkJpjY31SIQsPFDo7pHJZdsTGvH3F687Hm/NYccQ43eilZGczdV0uBzOLOHLCxuSVuWw76vyTufLm75FMK6+vzeFQZhGHM228uiaXvcnGvEuyIlYk2Ji5vXg7/MOOfFYk2Zwd0iVz5v/9a2ty2ZdqrItBF2Ld4Vw+2ulNRp6Nj3Z6s9ZFnlIrbw4vTnRjzs7idfj7bfksPep2iSKtHNO35bMrtYjkbBsTllpZsd846+Dyo968uDKHtFwbqxIKWXLIWPvgpYdh4cEC0nNtTNuQy5okh3vndbq9Kdm8vCqXIydsHMws4n/r8jie7fx9V3nzNzbJyosrc0k4YWN/RhHvrM/lhPXEJYr2wq0+auKzrXlk5Nn4Y18BfycUOTukcpm/v5CNyYWk5th4ZnkOS456Ozskh/2xN5cnl1g5nmMjNqWQX/cY64L8ikOF/Ly7+Bxgenweq464RvzlzWERkUvF4aM0d4uFJ556lvi4WDIy0mkWGVWiGNCgYUPq1K3Ltf0G4OF5cS/elfaahPNd9vlx9kxmzfjOoel7enri7f7PSZy3uwkP/j0n9PLv5elmOuOzkwI5Q3nyF8DzjDqIqyyHo7xO2zJ7uoOHa9R1HGIxgcUN8k5ew/BwM84FeSiO/XRe7saJ38tsxtvyz2cfiwkPN+fvu8qbvxZzEd6n/e4+7iYsHhcjMtd05nbYy2A5dCHO9X/vZXb+OnypWM7aB7vGxr+8OexxxnbUaPvg09dBb3cTBtoN4OEGbiYotJ36bKDgOXsffOb20JW5uZlKbr8s4GF2fg6Xex+MrcRxqK/FhJ+330WI7OI4+1zGOOsQlNx+WszGOgfwMJvwdIOCkzUcIx1Dg+tuf8qbwyIil0q5bvswmUxERkWf87vB1w2rlIDO5O8fQFZ2cZcT2dbiO4QCAoLwD/AnO+ufriis1mzCwmuUOp1BQ4bSt/+gEu3vG3Nnqe3vaOGByQQ5BTbubulJVLhXqW1FLhc9axXwSHtPft9bQNcabvSs7hp39ZQ3f3vUKGJ8Ow9+32ejRy0TV9cw1p0cfeu7c9xqIzHLxt0tPehR1zhPWAxo7MVrvWx8sjmPuoFmekYY60pSzxqQ0NyDtYmFDGhgoXvdAmeH5DA/Ly/GtgIvtzwKiuCulh7UD3X+vqu8+VvV15cH2lj5eHM+NuCuFhZqBRrnDr0L1T2siPvbeLLwQAE9I9zpHu4a2+FLoaqvLw+2sfLR5uK7Am9vYSHsX/R/36aGD8/FZPPDIR+G1LISU9P5+Qvlz+GrwgtIaO3JkoMFXFnHnV41XOMuT0cNb2ImO9+d1Bwb97TyoF1t46yDw5p5cTDTxpexeTQONtO9loGuRgK9athIiLawMamIoY0tdA/LBYzx+9cO9mZ8e/hwUz5uZriruQU/L+fncHnzt3F1HyZ0NPPB5jw83eCOaEupbV1Rt/Bcnujoxdzd+bQPd6NrmGtcFHbUlRFuJGZZ2J9exG3NPbi2kfPXIUf1quPBa718+HBTLmF+Zq6pa6x1p3tNC2NaFbH8cCHX1LO4zPazvDksInKpOFxQ+OXnH8s14X4DBpXdqBRFhYWY3YovQjWLjGLl38vp0rU7SxcvJCQ0lOphYRQWFvDZxx+yft0aAoOC2bF9O/0GDC51mhaLBYvF8Z3aqGgvRkUbZwcuUhla1AjklRrwfJc8PDxc55bg8uZv09rVeLk2TMo7tRwBFy+4i2BEpDcjIo1xAn0u97Xx5u7mbi61Djnqqoa+XNUQ8vJcKwccdXNzL25u7lr7rvLmL8Atzb25pblxc+BCdKrnS6d6xl0HL9TNzb25+V/6f9+sXnWeqgeP5uXh4RHi7HDsypvDHeoF0MHA6/CIKF9GRDk7iop7qL0397Uy5j64d2M/ejc+fd3xcXZI5eKK+66K7INHt/BidAvXOpZwVMuaQbSsCc90Mub2Z2ikN0Mjjbv9vCfGi9uizIaMvWddCz3rWlzut69IDouIXAoOFxS+/vIzzt+p0Ck2wHRBBYXXpkwmMiqafgMGMei6oSQmJvD4I/8lJCSUe8f9Fzc3NyJq12HU6Nv48P1pFBYU0rtPX1q2iqnwPEXkH650EHUhLpflMCKj//ZGj1+MT+vgv9fl8n9/uSyHERn9tzd6/OJ8Rl+HjBy/kWMH48cvInKpOFxQuG7o8NJfXnCBqlarzvQZc+yfH3lsgv1vPz8/Hh7/+DnH69tvoMMvgBYRERERERERERERkYpzuKAwdPiNFzMOERERERERERERERFxYeV6KfPp8vPz2btnN+lpadiw/TM8L4/Dhw8xfMRNlRKgiIiIiIiIiIiIiIg4X4UKCkmJCUx+cRJJiYmltlFBQURERERERERERETk8mGuyEjffPU5SYkJRDdvTo0aNQDo3qMnUdHRgI0Bg4ZUZowiIiIiIiIiIiIiIuJkFSoobIuPp2WrGJ54aiJRzVsAMObe+3n8yWepUbMWqanHKzVIERERERERERERERFxrgoVFHJzcwkMCgbAPyAAgBOZmZjNZho2asymDesrL0IREREREREREREREXG6ChUU6tarx7q1q9mxfRvhYeGAjbk/zyEh4Qg7tsVjdqvQZEVERERERERERERExEVV6Mr/oCHXk3UiiwXz/6Rdh074+wfw05zZPPzAfSQmJtKpc9fKjlNERERERERERERERJzIvSIjxbRuy8PjHyM9PR0PDw8efeIppn/9JelpqbRoFcPwETdVdpwiIiIiIiIiIiIiIuJEFSooALRp297+d/0GDZnw9MRKCUhERERERERERERERFxPhbo8euDee5g147tzfvfJR+8z8ekJFxSUiIiIiIiIiIiIiIi4lnI9oRAfFwtAcvJRDuzfa/98SmFhAXFbt5CSklJ5EYqIiIiIiIiIiIiIiNOVq6Aw6dknARNgYs3q1axZvfocrWxERkVXSnAiIiIiIiIiIiIiIuIaylVQaNO2HWBi3drVVK1Wjdq165b43s3NjbDwcK7tN6ASQxQREREREREREREREWcrV0Hh4fFPADBy+BBiWrfl1tvvuihBiYiIiIiIiIiIiIiIaylXQeGU/73zPj4+PpUdi4iIiIiIiIiIiIiIuCiHCwrTv/qCmDZtadoskl9+/vG8bU2YGH37nQ5Nd8XyZXz7zZdkZZ2gbfuO3HHXGCwWCwDJR5O4/957zhpn7LgH6da9Jw/dP5aEhCP24bfefhe9r+3n6CKJiIiIiIiIiIiIiIiDHC4oLFzwFxYPC02bRTLv91/LaO1YQSEzM4P3pk3ltjvvoWHDxrww6WkWzv+Ta/r0BSC0SlU++uwre/tdO3fy+pSXaBXTGoC0tFQeeWwCTZo2A8DTw9PRxRERERERERERERERkXJwuKBwx91jqFevAQD3jB1XKTPfvWsnNhv06HkFJpOJmJg2xMfF2gsKZrMZX18/e/sFf82ja7ce+PsHkJOTg9VqpXr1sBJtRERERERERERERESk8jlcUOjQsTMANpuNatWqExQURHiNmhc084yMDDy9PDGZTAD4+PqSlJR0zrZJiQmsXbOKya++CUBq6nEA3nrzNTIzM2nZKoZb77gbDw+Pc46fn59Pfn6+/bPVmn1BsYvIpaP8FTEu5a+IsSmHRYxL+StibMphEXFV5X4ps8lk4qXnJ9Kj5xXccfeYSg/oZG3hLHN//pHIqOZERNQGIDAgkFtuvYMGDRuRl5fH1DdepUatWvQfMPic4/84eyazZnxX6fGKyMWn/BUxLuWviLEph0WMS/krYmzKYRFxVeUuKAA0atyE/fv3XvDM/f39ybFaKSoqwmw2Y83OJiAw6Kx2GRkZLFm0gAceGm8fZnZzo1OXbgQFFbePjI7mwP79pc5r0JCh9O0/yP7Zas3mvjGOvThaRJxL+StiXMpfEWNTDosYl/JXxNiUwyLiqipUUAgJCWH5sqW8/OIkqoeFnfW9ycGXMjdo2Biz2cz8P/8gMro5GzeuZ8j1wykqLMTs5mZvN+/3XwgNrUJM6zb2YXGxW3l9yktMePo5/Pz92bFtG30HDCx1XhaLBYvFUs4lFRFXoPwVMS7lr4ixKYdFjEv5K2JsymERcVUVKigsX7YEgE0b15fSwrGCQkBAAGPG3s/0r7/gu+lf0a59R3r0vILXpkwmMiqafgMGkZeby7zff2Po8BH2dy0AtG7TlgEDh/Dma69QWFhA+w6d6NOnX0UWR0REREREREREREREylChgsI9Y8dVWgCdunSlU5euJYY98tgE+98enp588MkX5xz3hpGjuGHkqEqLRUREREREREREREREzq1CBYUePa+o7DhERERERERERERERMSFVaigAHDo4EHWrl1FemoaNmz24fl5eRw5cphnnnuxUgIUERERERERERERkctXSnIymZkZFR7f3z+AKlWrVmJEUpoKFRS2btnEKy+9QEFBwWlDbaf9bTpzFBERERERERERERGRElKSk3nowfvIz8ut8DQsHp68/ubbKipcAhUqKMz4bjoAw0fcSOzWrcRu3cI9Y8eRnHyUH2Z8x11jxlZqkCIiIiIiIiIiIiJy+cnMzCA/L5fw3mPxCKlZ7vHzjh8m4Y9pZGZmqKBwCVSooHDwwH7ad+zE4OuGkZ6eTuzWLXTv0QuTyUTsli38vWwJva64qrJjFREREREREREREZHLkEdITbyq1XN2GFIGc0VGcnNzp6AgH4CgoGAAUpKPAlA9LIzdu3ZVUngiIiIiIiIiIiIiIuIKKvSEQmRUNOvWrGbxwvnUrVcfsPHJR+/Ttn1H1q5eRWBQUOVGKSIiIiIiIiIiIiIiTlWhJxSGjxiJh4cH8fFxtGwVQ9269di0cQMff/Ae2dlZ9B8wqLLjFBERERERERERERERJ6rQEwo1a0Uw5fW3OHo0CYAnn5nEvD9+JTU1lZatYmjdpl2lBikiIiIiIiIiIiIiIs7lcEHh91/nnnP4vr17APDy8iY83JujSUn8/ttc+lzbv3IiFBERERERERERERERp3O4oPDFZx8DptOG2M74XHK4CgoiIiIiIiIiIiIiIpePcnZ5ZMNsNtO0WSTNIqMpKCggNzeHvNxcCgoKLk6EIiIiIiIiIiIiIiLidA4XFEbfdidLlyxiz+5dxMXGcvjwIbp268GVV11DzVoRFzNGERERERERERERERFxMocLCr2v7Ufva/tx+PAhlixcwLJli/nl5x/55eefaNioET16XUnnLt3w9va+mPGKiIiIiIiIiIiIiIgTlLPLI6hZsxY3jrqFETfdzJbNm1iyeCFrV6/i4w/e5cvPPqZ9h0706HUlUdHNL0a8IiIiIiIiIiIicg4pyclkZmZUeHx//wCqVK1aiRGJyOWm3AWFU0wmEy1atqJFy1bkWK38+uvP/DDjO5YtXcKypUv45vsfKjNOERERERERERERKUVKcjIPPXgf+Xm5FZ6GxcOT1998W0UFESlVhQsKAAX5+axZvYolixewZfMmioqK8PX1o3OXbg5PY8XyZXz7zZdkZZ2gbfuO3HHXGCwWi/37Hdu38cyTj5UY59MvpuPl7c2vc39i7k9zKCwspHvPXowcNRqTyXQhiyQiIiIiIiIiImI4mZkZ5OflEt57LB4hNcs9ft7xwyT8MY3MzAwVFESkVBUqKOzcsZ3Fixaw8u/lZGdnYzJB8xat6NHrCtq164D7aQWB88nMzOC9aVO57c57aNiwMS9MepqF8//kmj597W3S0lIJDa3Cy6+9aR/m5e3NwYMH+OqLz3hswlP4+Pgy6dknaRYZTes2bSuySCIiIiIiIiIiIobnEVITr2r1nB2GiFymHC4oHD92jCVLFrJ00UISEhIAG2Fh4fQfNJjuPa4gJCSk3DPfvWsnNhv06HkFJpOJmJg2xMfFliwopKYSFByMr69fiXG3xcUSHh5Oi5YxADRu0pRt8bEqKIiIiIiIiIiIiIiIXAQOFxTGjb0Lmw3MZhPNIqPo1KUr9eo3IC83lwP797F3zy5stn/at23XvsxpZmRk4Onlae+myMfXl6SkpBJtUo8fJzn5KPePvRuLhwfXDR1Ol67dT47rZW/n4+NLRkbpL53Jz88nPz/f/tlqzXZ00UXEyZS/Isal/BUxNuWwiHEpf0WMTTksIq7K4YKC7WS1oKjIRnxcLPFxsedtX9GXMp/5CoR2HToSHBJCZFRzVq38m/femUpkVPS5xz3PdH+cPZNZM76rUEwi4lzKXxHjUv6KGJtyWMS4lL8ixqYcFhFX5XBBoWmzyEp/4bG/vz85VitFRUWYzWas2dkEBAaVaBNapQrhNWri7e1N1WqDmfn9dI4cPox/gD/ZWVn2dlZrNmHhNUqd16AhQ+nbf1CJ9veNubNSl0dELg7lr4hxKX9FjE05LGJcyl8RY1MOi4ircrig8PTEFyp95g0aNsZsNjP/zz+IjG7Oxo3rGXL9cIoKCzG7uQHw6UcfkJp6nPsffJj169fh7u5OeI2aBAQE8NnHH7J+3RoCg4LZsX07/QYMLnVeFosFi4MvixYR16L8FTEu5a+IsSmHRYxL+StibMphEXFVDhcULoaAgADGjL2f6V9/wXfTv6Jd+4706HkFr02ZTGRUNP0GDGLULbfxwXtv8/CD9xEYFMTYcQ8SEhJCSEgIo0bfxofvT6OwoJDeffrSslWMMxdHREREREREREREROSy5dSCAkCnLl3p1KVriWGPPDbB/neVqlV54qmJ5xy3b7+B9O038KLGJyIiIiIiIiIiIiIiYHZ2ACIiIiIiIiIiIiIi4vpUUBARERERERERERERkTKpoCAiIiIiIiIiIiIiImVSQUFERERERERERERERMqkgoKIiIiIiIiIiIiIiJRJBQURERERERERERERESmTCgoiIiIiIiIiIiIiIlImFRRERERERERERERERKRMKiiIiIiIiIiIiIiIiEiZVFAQEREREREREREREZEyqaAgIiIiIiIiIiIiIiJlUkFBRERERERERERERETKpIKCiIiIiIiIiIiIiIiUSQUFEREREREREREREREpkwoKIiIiIiIiIiIiIiJSJhUURERERERERERERESkTO7ODmDF8mV8+82XZGWdoG37jtxx1xgsFov9+6TEBD547x127thBaGgoI28eTbv2HQF46P6xJCQcsbe99fa76H1tv0u+DCIiIiIiIiIiIiIilzunFhQyMzN4b9pUbrvzHho2bMwLk55m4fw/uaZPX3ubaW//j6CgIN54axrzfv+Nd6a+wUeffY27uztpaak88tgEmjRtBoCnh6ezFkVERERERERERERE5LLm1C6Pdu/aic0GPXpeQa2ICGJi2hAfF1uiTVR0c64fNoLQ0Cq0bNWK3NxccqxWcnJysFqtVK8ehq+vH76+frif9mSDiIiIiIiIiIiIiIhUHqc+oZCRkYGnlycmkwkAH19fkpKSSrQZPuIm+99/zfuDVjFt8PP3t3d19Nabr5GZmUnLVjHcesfdeHh4nHNe+fn55Ofn2z9brdmVvTgicpEof0WMS/krYmzKYRHjUv6KGJtyWERcldPfoXCmk7WFs8yc8S1xsVt5YfIUAAIDArnl1jto0LAReXl5TH3jVWrUqkX/AYPPOf6Ps2cya8Z3FylqEbmYlL8ixqX8FTE25bCIcSl/RYxNOSwirsqpBQV/f39yrFaKioowm81Ys7MJCAw6q92833/lt7k/8eSzzxNapSoAZjc3OnXpRlBQcfvI6GgO7N9f6rwGDRlK3/6D7J+t1mzuG3NnpS6PiFwcyl8R41L+ihibcljEuJS/IsamHBYRV+XUgkKDho0xm83M//MPIqObs3HjeoZcP5yiwkLMbm4ALF+2hK+++IwHHx5PtWrVyMo6gaeHJ3GxW3l9yktMePo5/Pz92bFtG30HDCx1XhaLBYvesSBiSMpfEeNS/ooYm3JYxLiUvyLGphwWEVfl1IJCQEAAY8bez/Svv+C76V/Rrn1HevS8gtemTCYyKpp+Awbx3TdfkZ+fx5TJz9vHGzN2HD16XcmAgUN487VXKCwsoH2HTvTp08+JSyMiIiIiIiIiIiIicvly+jsUOnXpSqcuXUsMe+SxCfa/p077oNRxbxg5ihtGjrposYmIiIiIiIiIiIiISDGzswMQERERERERERERERHXp4KCiIiIiIiIiIiIiIiUyeldHomIiIiIiIiIiIjIv1tGRgZWS2q5x8vPyLgI0UhpVFAQEREREREREREREadISy0uIqxetZIcj23lHt8rL41Gp01HLi4VFERERERERERERETEKbKyswCIrhFEYJXwco+fngK5Kf9MRy4uFRRERERERERERERExKl8PN0J9vEs93j5nu7kXoR45Nz0UmYRERERERERERERESmTCgoiIiIiIiIiIiIiIlImFRRERERERERERERERKRMKiiIiIiIiIiIiIiIiEiZ9FJmERERERERERERkUqQkZGB1ZJa7vHyMzIuQjQilU8FBREREREREREREZELkJZaXERYvWolOR7byj2+V14ajU6bjoirUkFBRERERERERERE5AJkZWcBEF0jiMAq4eUePz0FclP+mY6Iq1JBQURERERERERERKQS+Hi6E+zjWe7x8j3dyb0I8YhUNr2UWUREREREREREREREyqSCgoiIiIiIiIiIiIiIlMnpXR6tWL6Mb7/5kqysE7Rt35E77hqDxWKxf3/ixAnen/YWsVs3ExwSyu133kNUdHMAfp37E3N/mkNhYSHde/Zi5KjRmEwmZy2KiIiIiIiIiIiIiMhly6kFhczMDN6bNpXb7ryHhg0b88Kkp1k4/0+u6dPX3ubH2TM5diyFyVPeZOGCv3hn6htMfed9EhIT+OqLz3hswlP4+Pgy6dknaRYZTes2bSstvrjYeCgqJLReDar7hVTadEVc2YadCVhOJFLoV42WjWo6O5wKW7j7BAezzNTyKeKKhn7ODqfc4uJ3UpSXjXuVmjStWcXZ4ZTL5v0pmI4fwuwTQFST+s4Op9z+2GUlMdtELX+4sp6Xs8Mpt5nbcim0QfeauYQHBDg7nArJz8nhlz0FAPSr747Fy3j/Dxdiw87DWE4cJd+vGjEG3g5XRHp6OocPHgKgZkQtAgMDnRzRpbVg1wkOZrtRx6eIng19nR1OhS3YncWhLBO1vIu4opEB98Fx27EV5EJoDaIMtg/etC8Fc+ohzL5BRDWu6+xwym3ebiuJWVA3wEb3uj7ODqdcCgoK+HF3IWZgSJPy95vtKhIyU1l60BN3M1zX1Fj/BwAbdiVhyTxCoV8VWjaKcHY45Ra7bQ+2nEwIqkV03VBnh1Mu6xNziEuBAE8TAxsZLwf+2JtH4okiavqbuKqu8eIXEbmUnFpQ2L1rJzYb9Oh5BSaTiZiYNsTHxZYoKMTHxdKhY2eqVa/OVdf0Zs4PM0hKSmRbXCzh4eG0aBkDQOMmTdkWH1tpBYWDy34m8teHoKiQI1c+C1ffUinTFXFlG7ftp+Gfj+J/eDXWKk3YdO3/aBnV2NlhldsvO7J4cnkhG4/m0STEzOSCLAY3Nc6Fmb2rFxL501goyCWx0/3Euo0iKswYFzS2HEghbNW7VFv/KUWe/uwaOI2Gbbo4OyyHfRtrZfziXA5mFtGlphv5hTb6NPR2dlgOe2WVlccXW7HZYGJXb54yzk9fwusbCnliaR4Az3c18XhnJwd0CW2I3U3jP/8P38RNZIU1Z+M1r9MqsoGzw7pkMjf8SeQfjwFw6JoXCew13MkRXTo/xmfx5N+FbE3JI6qKmRcLshlowIt5c+KzeWJZPvHHi2hR1Y0XirLo38Q4++D9K+cR+fP9UJhHQvdHSQwYRpi/MW4s2rQvhZp/v0GVzdMp9A5m+8B3aRLT3tlhOWx6rJWHF+WQcMLGlXXceaIwiysaGGfdeWlVPs8ss2IywUs9ihjfwTjHD6d7f6Mnz/2dg9kEL6ebeNhAy7F++0EaLphAwP5l5AbXY0u/t2geHenssBy2e+0yon76D+RlkdT2Tja4301MLWOcA8QesfLBlkLe35iHjwVe72Xjnhjj3BAyY3suExZb2ZlaROvqbjzdxcSgRh7ODktExGU59R0KGRkZeHp52rsp8vH1JSMjo0SbzIwMvE7emejr42sfr3jcf3ZQPj5nj3u6/Px8srOz7f+s1uxS2+bl5RG29gMoKgSgxoo32XkguWILKWIg7gmb8T+8GgDvlO14H1rj5IiKlSd/AdYkwsajxfm7/XgRKxMvRZSVJzj+JyjIBSBsxVTc0kvftrkac3oS1dZ/Wvx3biaBexc5N6ByWp1YxMHMIgCWHy5kY7LNyRE57khmPh9vzqXIBjbg3Y05bE4qcHZY5c7fw6kneH9TPkU2KLLBe5vyOXAs6xJF63yeRzbim7gJAN/ELXgeXu/kiC6dw8cyqLX6LbAVga2IWqvf4mByurPDumTWHDWxNaV43xWbUsTqJCcHdFJ5c3h1ko3448Xb0c3JhS6zHI6quukbKCwuaIYvf52MlPMvrysxHz9Alc3TAXCzphK4b4mTIyqfFUcKSThRvN+dv7+ALceN05Xt7tQC3tuYg43ifddHm3JJsxpvH7zhsJVpG3OxAYU2+GRrHunZxskBS2IsAfuXAeCZuhfvA6udHFH5BO2ZD3nFxzzV136ER9pRJ0fkuP1WN97fWLztzM6HefvynRxR+axLKGRnavG+a31SIRtd4Bgayp/DIiKXitPfoXAmR16BUFqb84364+yZzJrxnUMxeHh4kFqlGcGJsQCcqNIMN09LGWOJGJ/ZrwqYzMUXcwCbb1UnR1SsPPkLUMPPjInii6oANXyNc0IKkBtc1/53TlBd8DLO3Xl4B1DgWxX3rOIibK5vdScHVD7VfP5ZV9zNUMPXqXX3cqnhb6FRkJkdJy/kNQ52I9CjEGfv6subv9W83WkUXMTe9OLlaBRsppqfyx2uXDTmgJLbXbN/NSdFcumF+nqQUSWSgOMHAcisEkmQn5uTo7p0ws7YV4X7uca+q7w5fHrcJor3yUaSFdoIn73FF+KtoY0pdDPO3dlmn0CKvAIw5xTfiJDna6ztRzWff9YVL3eobqDjt2o+0CjYjSMnii9CNgo2E+Tt/H1XefM3wFJI4yAzydnFxc1GQWYCfYzzpJS7fxUwu9lvDLQFGCsHcv3D7X/n+1UDL38nRlM+fu5Q09/E4cziM7BaBtv2n769MZsg3EXiL28Oi4hcKk49yvH39yfHaqWoqAiz2Yw1O5uAwKAz2gSQlV1cpc+2WgEICAjCP8Cf7Kx/7li0WrMJC69R6rwGDRlK3/6DSrS/b8ydpbbfHXMPNYLqYCrM42j93rSsHlRqW5HLRXjDpuwZ8DZ+iZvJDm2MrW5rZ4cElD9/b2iQR1aBF3HHimgUbKZ33VzAOCdD+xpdR4G7D+55GaTUvZKoOsa5KB/VMILt/d8mcN8i8n2qcKReH2o5O6hyuLpuAQVFXuzPKKJlVTduaWGcR7UBRjf3oF6QG0U2G91rWagT7Pz+X8ubvxYvL8Y1z6VxSHHsvWvl4+Xp/OW4VIKjotmXPxWfo3FkVW2GpW60s0O6ZLy8vIhv/xBhVSPBBon1+hDjbbz+9yuqT60cXunhxY7UIpqEmOldMwdw/sXs8ubwgNq55JxcjmYhZgY0dI3lcNT+qFHkeYfilp9NSv1riK5tnD7Mo5o2YOfA9wjYt5RcvzCO1utNbWcHVQ5X1iqksIsXhzKLaBvmxohI46w3/p7uPNjWk6gqZswmE73rOb+YAOXP3wbV/HiwTRYtq7vhbjbRy2Cv8WnWui17cqfhl7CB7JAGFNYzTpdfAEcaDKTI5I679RjpdXsR1dA474DoXseDl3sUseRgAf4eJnrUNtYNAYPr28gt9GJ3ahFRVdwY1bjI2SEB5c/h02VkZGC1pJZ7nvnn6flDROQUpx7pNGjYGLPZzPw//yAyujkbN65nyPXDKSosxOxWvANqFhnFyr+X06Vrd5YuXkhIaCjVw8IoLCzgs48/ZP26NQQGBbNj+3b6DRhc6rwsFgsWi+NPGbSNagBR9wIQXkZbkctFcHAwwZ37AH2cHUoJ5c3fwMBA/lvi/ME4xQSADs0ioNldABinlPCPJjHtIKYdAMY5DSrWJjyANgbe6A9r6sWwps6OoqTy5i9A/8hA+huny+NKVd0vBLr2B/o7OxSniIluBNGNgH/f8VfDsGAeCTt9iGvsu8qbw3WrBfNIiZuCXWM5HNW2WR1oNgaAsDLauqJGrTtC644AhiomAHSq40+nOs6OouIGN/ZkcGPXKoBXZB88NNKXoQbeB9fvdDVwtbPDqJC2jcOg8e2AMbc/N0V5cVOUs6OomHqhPjzmgvXjiuRwWmpxEWH1qpXkeGwr9zy98tJodNp0RETOxakFhYCAAMaMvZ/pX3/Bd9O/ol37jvToeQWvTZlMZFQ0/QYMYtB1Q0lMTODxR/5LSEgo9477L25ubkTUrsOo0bfx4fvTKCwopHefvrRsFePMxREREREREREREXGKUz18RNcIIrBK+W/PSE+B3JR/piMici5OfxazU5eudOrStcSwRx6bYP/bz8+Ph8c/fs5x+/YbSN9+Ay9qfCIiIiIiIiIiIkbh4+lOsE/5n5rK93Qn9yLEIyKXF9d404yIiIiIiIiIiIiIiLg0FRRERERERERERERERKRMKiiIiIiIiIiIiIiIiEiZVFAQEREREREREREREZEyOf2lzM5is9kAsFqznRyJiLF5e3tjMpku6TyVvyKV51LnsPJXpPJoHyxibNoHixiXq+6D83KLX6mcmZqMrchW7nmcSE8BIG7LFvu0ystkNmMrKqrQuEYff9eOHYDzfn/9dsU5kJ19/v2cM/L3cmOyndoi/cscO5bCfWPudHYYIob38eff4OPjc0nnqfwVqTyXOoeVvyKVR/tgEWPTPljEuLQPFjEuZ+Tv5eZfW1AoKioiNfU4Xl6lV6Ws1mzuG3Mnb7/3Ed7errOiKa7yUVzlV57YnFHZdSR/wbV/47IYOXYwdvxGjh3KH/+lzuF/Q/5Whn/z8v+blx0uj32w0f8PFb/zGDl20D7YVRg5fiPHDsaO39XzF7QPNgIjx2/k2MH1j6EvN//aLo/MZjOhoVUcauvt7eOSlSvFVT6Kq/xcNbby5C+47nI4wsixg7HjN3Ls4Lrx/5vytzL8m5f/37zs4LrLfzkcQztK8TuPkWMH143/37YPNnL8Ro4djB2/K8eufbBxGDl+I8cOxo/fKPRSZhERERERERERERERKZMKCiIiIiIiIiIiIiIiUiYVFM7DYrFw/bAbsFgszg6lBMVVPoqr/Fw5tvIw8nIYOXYwdvxGjh2MH/8pl8tyVNS/efn/zcsOl8fyG30ZFL/zGDl2MH78pxh9OYwcv5FjB2PHb+TYT2f05VD8zmPk2MH48RvNv/alzCIiIiIiIiIiIiIi4jg9oSAiIiIiIiIiIiIiImVSQUFERERERERERERERMqkgoKIiIiIiIiIiIiIiJTJ3dkBuIoVy5fx7TdfkpV1grbtO3LHXWNKvMjjxIkTvD/tLWK3biY4JJTb77yHqOjmTo8rKTGBD957h507dhAaGsrIm0fTrn1Hp8d1yv59e5nw2P8x+LqhDB1+o0vEtX/fXqZ//SU7tsfzxFMTadiosdPjSktL492332T7tngCAgK5YeQounTtftHjAkhPT2P5siUsWbSQe8c9SETtOiW+d9a6f6HKWi5X9vOPs/l17k/k5ubQolUMY8bej5eXl7PDckhhYSHfTf+KBX/NAyCmdVvuGnMvHh4eTo6sfN587RVWrfyb6TPmODuUcnvp+WfZvGmj/XO/AYMYdcttzguoAhzdx1yujLz9ulBG3v5dqMtl+wnGz2Ej56CRc+hyyQEjH0OA8tfZjJrDl0v+grFy2FWvYzmqrPh3bN/GM08+VmKcT7+Yjpe396UO9ZyMfC2lrNhd+bcvazvpyr/75URPKACZmRm8N20qQ4YO59lJk9m0cT0L5/9Zos2Ps2dy7FgKk6e8SfsOnXhn6hsU5Oc7Pa5pb/8PPz8/3nhrGu07di6Oq6DA6XEBFBUW8sF773Cp3vvtSFxHk5KY+PQEgoKDmfTSFOrXb+AScf04eyapqalMeX0q11zbl3ffnkpubu5Fj81qtTLuP3ezcvly9u/be842zlj3L5Qjy+WqNm/ayHfTv2bsuAeZ+MLLbI+P5/dff3Z2WA5btnQxC/76k0cee5KJL7zM1i2b7ScWRrF2zWrWrF7p7DAqLDU1ldvuvJuPPvuKjz77iuEjbnJ2SOXi6D7mcmXk7deFMvr270JdDttPMH4OGzkHjZ5Dl0MOGP0YQvnrXEbO4cshf8FYOeyq17Ec5Uj8aWmphIZWsZ/XfPTZVy5xQRuMfS3Fkdhd9bd3ZDvpqr/75UYFBWD3rp3YbNCj5xXUioggJqYN8XGxJdrEx8XSoWNnqlWvzlXX9CY19ThJSYlOjysqujnXDxtBaGgVWrZqRW5uLjlWq9PjAvj115/x8PCkTp26FzWe8sT1y9wfCQsL4+4x91KzZi3Mbm4uEZfZZMbLy4vgkFBCQkJxd3e7JIUYDw8P3nr3A8Y9+FCpbZyx7l8oR5bLVbm7uzNy1C00b9GSiIjahNeoQXpaurPDcliPnlfw0Wdf0aRpM4KCgnB3d8ftEuRZZcnOzubTj9+n97X9nB1KhaWlplKtWnV8ff3w9fUz3J1hju5jLldG3n5dKKNv/y6U0befpxg9h42cg0bPIaPnwOVwDKH8dS4j57DR8xeMl8Oueh3LUY7En5aaSlBwsP28xtfXz0nRns3I11Icid1Vf3tHtpOu+rtfblRQADIyMvD08sRkMgHg4+tLRkZGiTaZGRn2R2h8fXzt4zk7ruEjbqL2yQv2f837g1YxbfDz93d6XEeTkvjxh1ncPWYsnGx3sTkS187t2/D08uLx8Q/x4H1jmPf7ry4R1/XDbuDYsRRuu3kEb735Grfdec8lebTVzc2NwMCg87Zxxrp/oRxZLlcVGRVN3/4DgeLuuXbt3EGXbpem+6vKNP7hB7j79lsICw/nqmv6ODsch03/6guat2hFdPMWzg6lQgry8zlxIpOvv/ycsXffzhuvvkxmpmvn65kc2WZezoy8/bpQl8v270IZdft5itFz2Mg5eLnkkFFzwOjHEKD8dbbLIYeNmr9gvBx21etYjnIk/tTjx0lOPsr9Y+/m4QfvY/myJc4I9ZyMfC3Fkdhd9bd3ZDvpqr/75UYFhVI4cg38El0nd2ieM2d8S1zsVu68e8ylDeikM+P66INp9O0/kPAaNZ0SzylnxpWVlUVRURF3j7mXa/sN4LNPPuTwoYNOj2vWjO8IDAhk0ouvMGzESL758nOys7IueVyOcsa6/29z7FgKUya/QL/+gy7Jez4q26NPPMXjTz7D/n37WLxogbPDcci2+DjWrlnFqJtvdXYoFWcycesdd3Hz6Nt56JHHOHBgPzO//9bZUV0wbXP+XYy+/btQRtx+lkU5fGkZPYeMmAOXxTFEKZS/l56Rc9iI+QuXTw676nUsR50ZW7sOHbl+6A2Mf/wpOnfpxnvvTCU19bhzgqskrvz7n87Vf/vybieN8rsbiQoKgL+/PzlWK0VFRQBYs7MJOKNa5+8fQFZ28QXe7JNdCgUElGzjjLgA5v3+K7/N/YlHJzxNaJWqFzUmR+LauWM7WzZv4ucff+CO0SPZv28vP835gR9nz3JqXAABgYG0bdeBBg0b0fvaflgsFg4ePOD0uDZtXE+nLt2oW68+/fsPIiMjnT17dl/UuBzljHX/3y4jI4MXJz1LVHRzht9orP7v9+/fx+ZNGwkNrUJ085ZERkcTF7vV2WE5ZPasGWRmZvLAffcw9Y3XALhj9EgnR1U+RUVFtG3bgRYtW9GwUWPatmvPgf37nR1WuTi675PLk5G3fxfKyNvP0ymHncvIOWTkHLgcjiFA+esKjJrDRs5fMGYOu+p1LEc5En9olSp069GLWhER9B84mIKCAo4cPuyEaCvGlX//srjyb1/WdtLIv7uRqKAANGjYGLPZzPw//+Dw4UNs3LieyKhoigoL7W2aRUax8u/lJCUlsuCvPwgJDaV6WJjT41q+bAlfffEZ997/ENWqVSMr68RFf9lIWXHVqVuPqe+8z8uvvsnkKW9Qs1YEV17dm6uu6e3UuABi2rRl6ZJFJCYksHTJIgoKCqhdu67T46pZK4L169aQkpzM0qWLcXNzIzw8/KLGdT7OXvf/zXKsVl55cRJVqlRh1OjbyM7OIjs729lhOezAvr288epkdu7YzsGDB9i5Yzt169VzdlgO+c+99/PG1HeYPOUNRt9+JwCTp7zh5KjKJykxgXvH3MGK5ctISkpk86aN1K5Tx9lhlUtp20y5/Bl9+3ehjLz9PJ1y2HmMnkNGzoHL4RgClL/OZuQcNnL+gjFz2FWvYznKkfg//egDJr8wkWMpySxZvBB3d3en94JRFqP8/udihN++tO2kkX93o3J3dgCuICAggDFj72f611/w3fSvaNe+Iz16XsFrUyYTGRVNvwGDGHTdUBITE3j8kf8SEhLKveP+e9FfMuRIXN998xX5+XlMmfy8fbwxY8fRo9eVTo2rarXq9vbu7u74+vpe9Je4OBJX/wGDOZ6SwpOP/x/e3j7cPeZeatS8uBtFR+K65dbb+eC9d/i//44jICCAe8aOuyRPm5TG2ev+v9mqVSvYvXsnAHfffgsAVapW5a1pHzozLId17d6T/fv3MWXyC9hsNjp27sI1vfs6OyyHBAUH2/8ODCx+cun0bZkRRNSuw+jb7+SrLz7Fas2meYuWDLvBte+uOlNp20y5/Bl9+3ehjLz9PJ1y2HmMnkNGzoHL4RgClL/OZuQcNnL+gjFz2FWvYznKkfhH3XIbH7z3Ng8/eB+BQUGMHfcgISEhzg79vIzy+5+LEX770raTtWvXNezvblQmm81mc3YQIiIiIiIiIiIiIiLi2tTlkYiIiIiIiIiIiIiIlEkFBRERERERERERERERKZMKCiIiIiIiIiIiIiIiUiYVFEREREREREREREREpEwqKIiIiIiIiIiIiIiISJlUUBARERERERERERERkTK5OzsAcU1FRUX8Ne93Fvw1j8TEBLy9fWgWFc2Q64cREVH7os47+WgS9997T5ntnnp2Eu7uFl6b8hJR0S24/8GHL2pcIkY3buxdpCQnM/Wd96larXqZ7eNitzDp2aeIad2W8Y8/WWq7d9/+H0sWL2TM2HH06HVlZYZcbs89M4H4uFien/wqDRo0dGosYnzxcbH8OHsW+/fvJetEFtXDwujarQd9+w/EYrFc9PnfOGwwnp5efPbVt+dtd/zYMWbPmsGG9WvJzMygSpWqdO7anQEDB+Ph6Vkpsfzv9SnEx8Xy0COP0bhJ00qZ5inahkhFnVp3zqdZZBRPT3zhksTx4EPjadO2HbePHklBQSGffP41Xt7eAKSlpvKfu28jJDSUd9772D7upo0bmPzCRCKjmvPUs5MuKI6Z309n1ozvuOnmW+k/cPA521ysXH7p+WfZvGkjffsP5ObRt5f47rdffuaLzz4monYdJk95A7NZ97RJxZ3aTptMZt6a9j6hVaqe1eaP337hs08+BODmW2+nb7+BFz0uR/bZp28rOnTq7PB0AabPmFMJUYq4tkud36tW/M1HH75Lz15XctPNt1Z4OnDufXB+fj5/zvuNJQsXkJSUiJubGw0aNqL/wCE0b9HyguYn4mw6mpNzmvrGq3z68QdkZGTQsXNXGjZqxOqVf/P0E+PZFh97Ueft7e3DNb372v81aNAIgAYNGpUYHhwSSlhYOFf3vpbOXbpd1JhEROTfZcnihUx69km2xcfSpEkzOnfpSl5uLt9+8yWvvvwiNpuNNatXcuOwwbz79v+cFmdCwhEmPP5//PXn71SpUpUu3XpgsViY+f10Jk18irzc3HJP847RI+0XME7p0q07V/XuQ1hYeCVFLnLholu0LHFseMrpw9q17+jw9B77vwe5cdhgko8mVTgmd4uFevUbYLMVsW/fHvvw2NgtQHEBMCkxwT58z+5dAJVeqCvNxcrlG24chclkYv6ff5CRnm4fXpCfz9yf5gAwfMRNKiZIpbHZiliyeNE5v1tayvDKkJV1ghuHDWbc2Lsu2jxE/u0uVX7XrV+fa665ltZt2tmHvfbKi9w4bDBxJ/fbFVVUVMQrLz3Pl599Qm5uLh06dSEqujnxcbG8OOkZli9dfMHz1PZInElPKMhZFi9awKqVf1MrojYTn5+Mj48PAJs3beSl55/l3Xem8tqb7+DuXvmrj81mw8fXl9vuvNs+bOb309m9eyetWrdm6PAbzxpn6LARlR6HiIj8u8347htsNhtPPDXRfqGvID+fJ58Yz+ZNG9i0cb2TIyz20fvTSEtNZcj1wxg+4iageF867a03WbZ0MXNmz7QPvxBt23WgbbsOFzwdkcrUrXtPunXvaf88749fAUocRzpD48ZN2bF9G3t276ZpsygA4mO32r+Pi91K9ZMX9E8VFJo0rXhBwWazYbPZHGp7sXK5foOGtGvfkdWrVvDL3J+48aabAVi4cD7Hjx+jYaPGtG3XvtLne7rCwkLc3Nwu6jzEdZhMZpYsXsiQ64eVGH740EF2796JyWTGZityUnQiciEudn6f2m9Wrx7GsBEjLzTcc9oWH8vWLZuoFVGbl15+DfeTTzfv2rmDp54Yz1dffEbnrt0xmUwXZf4iF5sKCnKW+fP+AGDQkOvtxQSAFi1bERnVnLjYLfz15+98/slHVKlSlbfe/dDe5s3XX2HVir+59/7/0rVbDwoLC5nzw0wWL5xPaupxqlcPY8Dg6+jR8woAFi+cz3vT3qJLtx6kHj/Gzh3beWzC00RGNXco1jO7ZDnVXVJkVHOaNG3KogXzOXEik3r1G3DPf8YRHx/Lz3N+IDX1ODVq1uLm0bcTGRVtn15iQgLTv/6c2K1bKCwspGmzKG4efTs1atasjJ9WxKXs3rWT77/9hl07t1NYWESTpk254cZR1D+jm4+8vFw+/egDVq9aQW5uLm3btefW2+/Cx9e3XPPbsnkTM7+fzr69e/Dy8qZd+w7cdMtteHt78/b/Xmf5siXcM3YcPU92eXLo4EEeeWgcYWHhvPHWu0DxhZfvpn/Fju3bcHNzo0Wr1tw8+jaCg0Mq50cROenUHbZVq1WzD3O3WBh2w43Ex8Xy05wfiI8rfmJvyeKFLFm8kKeenURkVHMKCgqYM3smSxcv5PixYwQHh9C9Zy8GXzesRDF+y+ZNzPr+W/bu3Y23tw+RUdHcOOoWqlatxrn8/utcPv/0I6pXD+O5F18h60QmcbFb8fX1ZeDg6+3tTCYTw0eMZPmypSyc/xdDh9+I2Wxm3Ni7yMzIYPTtdzH3pzkkHz1KrYgIbrn1Dpo2i7Tvk0+5cdhguvfoxX/ue+CcXQElJBzh26+/JC52K7m5udSv34Drht1Ai5atgH+6MGzeoiUtWsXwx2+/kJmRQd169bnz7rHUiogo1/+JtiFyITZt3MDsWd+zb+9e3NzMRDdvwY03jSYsPNx+PHnK/ffeU6KrpCWLF/L7r3M5dPAgvn6+tGjRihtHjSYoKOic82rctCn8/E+xAIqLCNWqVefYsWPExW6l15VXA7Bnzy5MJhMNGzVxKFYo/fj5TJmZGTz9xKMkJibYu4U4M5ftXY7dez8rli8jPm4rfn7+9LryqhI38hw5fJhPP/6A7dvi8fHx4YqrrmbD+nXs27vH3hXL8BtvYu2aVfz5x68MGDQELy8vfpozCyh+guF058tngLy8PH78YSbLly3h+PFjBIeE0K1HLwYPGWrfjt44bDDBwSF07tqNpYsX0SqmNf+57wFSU4/z7ddfsnnTRnJyrNSKqM2Q64eVuANVjK9ps2bEx8WyY/u2Ek/4LFlU3A1as8hI4k4r5OVYrXz37desXrmCjIx0qlWrztV9rqXPtf3tbcaNvYvU46n836NP8O03X3Lk8GGqVqvGiJGjaNe+o71LE4CU5GRuHDaY64fdUCJXli5eyA8zv+f48WNERNTh1jvuomGjxmfFv3nTBl56fiIREbV55fWp9uHjH7qfgwcPMOHp54hu3uKs8RzN2bS0NL795ks2rFtLbm4uNWvVYsj1w0oUFB3dj+vcWi618ub34UMH+f7bb06uawVE1K7DsBtG2rsVKm2/mXz0KO9Ne4trevel/8BBJbrenvTsU1SpWpW3phVf79qwfi0/zp7Fvr178PDwpGmzSEaO+mfffKb0k+cSISEh9mICQMNGjblu6A3k5uZw6NBBxj90f4Xmeb7t0bm6YHvtlRdZu2a1/XwFis8t/pz3OynJRwkKCqZz1+4MuW5opXWZKpc3PXMqJRQWFrJr104AmjaNPOv7ZpHFwzLS06lXrz4pKcns37/PPu6WTRuxWDxo07b4DqS3//c6M7+fjp+fH1269SAnJ4f33pnK2jWrS0x3+dLF5OXl0a17TwKDgi94OeJit7BsyWKiW7SkWvUwdmzfxrNPPc6Xn31C4yZNadykGfv27uGN117GarUCcCwlmacmjGfd2jVEN29Ji5YxbNm8kReee5rcCnQZIeLKdu/excSnnyB262aim7ekabNINm/ayLNPPcHePbtLtI3duoXY2C20jGmNf4A/S5cs4v133y7X/NavW8NLz0/kyJHDdOzUhRo1azH/r3n2rmI6dela3G7tGvs4G9avBaBj5+Lv9u7ZzcSnn2D7tnjatOtAo8ZNWfn3MqZMfsHhOzNFHNUqpg0AL784iU0bN1BUVHwXVJu27Rl1y220a9/R3qZGzVr2rvgA3nrzNWZ9/y3ubu506tyVwqJCZs34jnemvmGf/uZNxf2m7927mzbtOhBRuw4r/l7G888+RXZ29lnxrF2zii8++wQ/f38enfA0AQEBbNsWD0CduvXx8vIq0b5qteqEVgklLS21RPctubm5fPX5J9SuXYcGDRuxd89uJr/wHGmpqdSsFcE1vfvaL9Zd07sv0aX075qSnMxTj49n9aoVNGzUmJjWrdm1aweTX3iO9evWlGgbu3ULv/8yl+bNW1KjRk22b4vn3XfK102UtiFyIdauWc3LL05i/769tG7bjojadVi9aiVPT3iUlORkgkNCuaZ3X/z9AwDo1qOXvaukP377hXff/h9pqal06dqN6tXCWLJ4IW+8OrnU+Z26+HGqoJCaepyEhCPEtGlLg4YNiYsrvgiSlpbG8WPHqFGzFn5+fg7FerrzHT/n5+fz2ssvkZiYQJ9r+5fZx/SH772Dm5uZ9h06ceJEJrNmfMf6dcU5lJebywuTnmbrlk1E1K5NVHRz5v85j31795SYRs2atejWvSdWq5XffvmZpUsWkZKcTHTzFiUujJaVzwDvTH2DH2Z9j7ePD1279cCEiVnff8us70v2T5+aepylSxYR06YtTSOjsNlsvPT8RJYsXkjNWrVo264DBw/s59WXX2LL5k3n/Q3EWDqd3LYvXjjfPqyoqIhlSxcTGBhEs9MubBcWFvLipGf5/de5BAYG0qFTFzIyM/j8k4/45qvPS0y3sLCAt958lYiI4nX9yOFDvDP1DTLS02nQsBFXnCwGent7F3fP27CRfdzc3By++uIzmjSLpF79BuzevZP/vT6FgoKCs+KPbt6S4OAQDh48YO8GLSU5mYMHDxAcHFLiwvy5nC9nc3JymPTMBBYvnE94eA3ad+hIwpHDvPbKS/bz8PLsx3VuLZdaefL72LEUnp7wKOvWriYqOprWbdqxb+8eXnlpEgcP7C8x3fPtN091vV3t5LsG27brQPcexTfCrlu7mimTX+DQwYN06NiZevUbsGb1Sia/MLHU7kWbRUbj6enJ5k0b+eiDd0lKSrR/N+yGGxl1y20EBwVXeJ5lbY/KMvenOXz+6Ufk5ebSuUs33C0W5vwwg48+eNfhaci/m55QkBIyMtLtj44Fh5x9t96pO/jS09Lo2KUre/fuYcO6NdSpU5ft2+LIzs6mfYeOeHt7sy0+lpUrlhMZ1Zwnn3kOk8lEWmoq9/3nTn6Y+V2Jx55btIzhsQlPV9rjXoGBQbw05XV8ff2wWq3cc8doMjMzeOChR+jYqQsAzz75ONu3x7N/316aNotk5vffciIzs8RLIef+PIevv/iMRQv+ove1/SolNhFX8M2Xn5Ofn8/YcQ/au4uYPWsG33/7Nd9/+zWPPvHPnY41a9Zi8iuv426xkJ2dzcMP3Mua1StJOHKY8BqO3WH0xacfYzabee6FlwkPrwHAlMnPs2b1Sg4e2E/LljH4+vqyZfNG8vPzsVgs9ouBnToX5+zXX35GXl5eibsqPv34A+b9/isb1q/VnYdSqe685z9kZmYQHxfL5BcmEhQcTMeOXeh9bT/CwsO5tt8AqlStysYN62jYsJG9i5X4uFhWr1pBRERtnn9pCh6enmRmZvDIf+9n5Yrl9Ns5iIaNGvPt119SVFTEw+Mfp0XLGAA+ePdtFi74iw3r1tClWw97LLt37+Lt/72Ou7s7/zf+CXsOpaenAefeX0PxPjslOZn09HR79yoAjz7xtP2C56k7Hf/47RduGDmKho0as2zpIgoKCs7bbczMGd+SlXWCYSNGct31wwFYtnQx70x9g2++/LxEPvr4+jJ5yhv4+fuTn5/Pf+66jT27d5Gbm4ung3dAaRsiF+Krzz/BZivi0Seepmmz4ptjPnjvHRbO/5Mf58zijrvGcNudd7N9WxyZmRkMGz6CqidP7q05Vrr36MWgIUOpUbMmNpuNhx+4lx3bt3H82DFCQkPPml9gYBDVq4eRmJhAdnY2cVuL+0SObt4CHx8fZs+aQVJiAocPHwKgyWl3XzoS6ymlHT+f6vZs+/Z42rZrz823lnxJ8rkMuX441w+7AYDaP9blm68+Jz5uK63btGXhgr84fuwYLVu1ZvzjT2I2m0k+msTDD95Hfn5+ielcP3wEy5ct4Y/f5uJ7skhy5tMJZeVzzVoRePv4cOVV13DrHXfj7u7O8WPHuHfMHaxcsZwbRv4zPTc3N1546VWqVC1+cWdSUiIHD+ynVkRtnnym+CXXy5cuZs7sWezYvk0vwbyM1KvfgLDwGqxcsZzRt9+Fh4cHW7ds5vjxY/QbMIjC/H8u4i9ftoSdO7fTomUrHn3iacxmM0mJCTzy0P38Ovcn+vQdQMhp+9L/e3QCzSKLuyt7+cVJbNywjt27dxLTui2NmzRlwfw/8fXzO2s/6ebmxvOTp1C1ajVsNhuP/Hcchw8fIikpkZo1a5Voazab6dKtO3N/msOaNavoP2Cw/UJ+567dynzfyPlydtHCvzhy5DCdu3Rj3IMPA7AtPo6JTz/BvN9/oW279uXaj+vcWi618uR38tEk2rbrQNNmkfan/06d165ds5qI2nXsbc/cb+7asd3+nZ+/P7fdeTevvfIiR48mcW2//vbjxbS0NLp178kVV11Dk6bNAHjhuWfYumUTe/fusQ87XVBQEP/9v8d4e+rrzP/zD+b/+QcNGzWmW/ee9Ox1JR6enhc0z7K2R2VZt7a4uPjg/z1KgwYNyc7O5rmnn+Dw4UMUFRXpnUdSJhUUpARbke20v4vgPP2QdurUhelffcH6tWsZfN0wNqxbB/xzJ+CmjRsBKCwo4LOP/+kWyd3dwqGDB0rcDRgWFl6pfcf5BwTg61t8EuPt7U1gUCApycklulKqHhbG9u3x9gsymzcVx7t9Wzx7dhffoX38eArAWZVtESPLzy8gPi4Wb29vunTtbh/eu09fvv/2a7Zv21aifbXqYfbHNH18fGjdpi0L5v/Jgf37HSooJCYkkJSUiJ+fP7//Mtc+PC0tDYADB/YTUbsObdt1YPGiBcTFbqVRo8bs2L6NmjVrUbtOXfLz84mLjcXNzZ1VK1awasUKAA4dOmifhi4GSmXy9w/g6YkvsHHDOhYvXMCG9Wv5/be5/DnvN0bePLrUu303b9wAQPeeV9gfF/b3D6BTl678/uvc4vW6VgR79+7B3z/AXkwAGDr8Rjp16UZYWJh9WGFhAa9PeYnc3FzGjB1X4oTl1D7bVlS+PmTDTl7AA+h15dUsWbyQffv2lmsap5bzqqv72Id16dqdzz7+kMOHD3HixAn78KCgYPz8/QGwWCyEVqlCVtYJMjLSS+3e6XTahsiFSEg4QlJSIrUiatsv0ANc0/taFs7/kx3bt51nbBg8ZChZWSdYvXIFS5csIjs7i5ycHADS0lLPWVAAaNy0GUlJiezds5u42K2YzWYiI6Px8vJm9qwZxMVu5dix4uPMUwW+8sZa2vHz/D//sF/AvO+Bhx26KFCr1j9dkJ16YXNmRgZQXNQE6HXFVfZpVa1WHYvFclZBoWrValx1dR9+/20uWVlZtG3XvkR3L47m85ix40hKTOCveb9z7FgKebl5J9ullpifu7vFXkyA4kJqcHAICUeOMPP76bRp14HOXbuXKNLK5SEvL5cePXvx3fSvWbN6JV26dmfJ4uLuUHr0upIFf82ztz21z7riymvs63D1sHBatoph7ZrV7N65g5AO/7zAveY58iHjZD6cj7u7xb5fM5lMhIXX4PDhQ6SnpZ1VUIDi98DM/WkOa1efKigUF8IdWV/Pl7PxsbFnTadJ02Y88dREeyG/PPtxnVvLpVae/G7aLIomTSPZtHEDc2bPJD01jUOHDgBn7zMqet3pyquuoVu3HqxevZItmzeSmZnJ8ePHzjmP07VsFcP/3n6fRQv+4u9lS9m1cwe7du5g7k9zeOSxCSWKHZU1T0fVb9CQbfFxzPj2G3pf25dmzaKY/OqbFzxd+fdQQUFKCAgMxGQyYbPZSE09br8765TU1OMABAYFUbVadRo2asyuXTtJT09j/fq1eHp62k/I009u5LZvj2f79viz5pV12kGKU52sa5zaKC9c8NdZTVJTL3yDLeIqMjMzsNmKCAwKLnGRwcfXF4vFw36hpDQBgUEApKYdd2h+p3LrxIlM+wszS3x/Mr86du7K4kULWL9uDdlZWRQWFtLxZDcmp56eKiwsOu80RCpbq5g2tIppQ05ODksWLeCrLz7lq88/pUmTs+9EAkjPKO4vNTi45GPUp57wy8mxkp1VvP/zDwgo0SYkNPSsi5MFBQUcP1Z88hAfF2u/yw+K98Xwz775TPZ9dmBgqcsXEFD8XXo5T0wyMtJxc3Mn4LRlMJlMBAYFkZV1gtwca6nj2k/jHOxmSNsQuRCn3odyZk4GnZaT57Nh/VqmvvHqOfeN51uDGzdpytLFC9mzexdxsVupV78BPr6+NG7SFIvFg7jYrWRlZdnbVkasp5zqViE5+SgpKcnnvJB5XieT9NTNP/a4Snka6kyDrx/K778VFwsGDRla4jtH83nm99P5YeaMs166WdZmw8PDg6cmPs83X37Oj7NnMWvGdwQEBnLV1X0Ycn3J99iIseXm5tK9xxV8/+10lixaSOs27Vi7eiUNGjYiIqJ2iW5I0ktZh4OCHMitUzutCnSN9891y3OPW7tOXerUrceO7dtJPppEXOxWatasRb169cs5o1MhFs8n6+Rxxpn76NOf0LmQ/fh56dxaKkF58jsr6wQvTHyavWd0wwecf0ddDnt27+LlFyeRcfI4vzzz8PHxoW//gfTtP/Dke0W+YPWqFbz15msl3p9SmfN0xI033YKXlzcL/prHKy89j5ubO61iWnPTLbfanx4UOR8dUUkJ7u7u1K1Xn717dhMbu5WeZxQUtsXHAdCocfGL4zp17squnTuY99uvHDl8iE6du9rvevD2Ln6h84iRNzNoyPW4Om9vH7KyTjDtg0/0ckYxtI0b1rNnzy5iYtpQr34D+/D8vOK7CKtWrYbJZCYzIx2bzWa/S+PEiRPk5+cRdMaFjDOdyMwEih9/doT3yZe716hRk9f+906p7Zq3aImfvz/r163BerIP+VP9Z57annh6evHx51/jdp6np0Qu1I7t2/j5xx9o0Kgxg09eDPPy8uKaPn05ePAAf837nbjYred8CdupftBPXbw4JfX4qYv7Qfj6+mEymex3Ep5SWFhIfl4e7hZLiYteAwddx9/Ll7J40QLatu9gf6Fio5N3/e7bu4fs7Gx8TuYaFF9IPJZyjMDAoLNuDjhdWtqpGwXK9/4iXz8/MtLTOXHihH2ZbTYb6WmpmEwm/AMCy12kKI22IXIhSs3J1H9y8ny++PRjcnJyuOXWO+jesxe+vn72FxufT+OTx8rr1q4mMTHBfizs4eFBo8ZNiI+LpaCgAH//APvTfhca6ylVqlalU+eu/PzjbKa99SbPvfDyBa3zpwoc6SefIijL6XGeWdB0JJ+PHUvhh5nf4+3tzQMPjadp02Z4eHpy47DBDs0/PLwGD49/nNzcXLZvi+PnH2fzw8zvsNmKGD7iJoemIa6vsLCQkNBQmrdoyZbNm/jtl5/Izc21F95Pf2+BPbfOWIdPv1nOWbp178lXX3zKZ598RH5+XqU8TePnV/xU4JlPVeRYrZjMZjw9PS/6flzn1nIhypPf837/jb1799CiZStuv/MeqlatxtIli3hv2luVFs/0r78kIyOdAYOG0H/AYPwDAnjvnan2pybOZfasGezZvZNBQ4ban9QLCw/ngf/+H3feNoqDBw+QkZ5OQCk3/lRknqcUn9+fv+rg7u7OsBtuZOjwERw6eICVK/5m9qwZ7N+/l/+99R5mHStLGdQplpzlyqt7A/DznB/sdzcAbN2ymditW6hStSrRzYvvbujYqQsmk4mff5oD/HPiDhAZXfyinAXz55WYzqn+UV1N1Ml4555cFijeUf0696dzvkhLxFUdOniAGd9+w7zf/7nzb9/ePaSnp+Hr60dQUBCNGjcmKyuLlSuW29v89efvADRvXrJ/4aTEBPLyirsayMnJYf36NZhMJurUqedQPBG1IggIDOTIkcMlXsielpbGgvl/2j+7ubnRvn1HUpKTWbnib2rXqWu/q9LHx4d69eqTm5vDn3/8Zh8n5+SLH/VCValMAQGBrFu7ll9++vGsl6AmHz0KFD/Rc+oCXWFhof37U/vHpUsW2bsCOXHiBCtXLMdkMhHdvAVe3t7UrVefzMyMEi8J/frLz7jtlhtZdNoL6Dw9vbhx1C3cM/Y+TCYTH74/zX63cETtOjRu0hSr1cqcH2bYx7HZbMz8bjo2WxE9el15VncnyclH7X8vWlA8r3r1/7kb8tQJxPn2fae2Ewvm//PI+d/Ll5KVlUWTps3w8PAoddzy0jZELkSNmrUICQ3lwP597Nq5wz58/p9/ACX3efZ1/7ScTk1NxWQyc+VV1+Dr60d+fv5ZF/zPpVZEbXx8fNh+8uXpp7+UOCo6mmPHUkhPT7M/nVDeWM+nd59+3HjTLTSLjGLP7l3MPm37UBH1T77kcdHCv+y5cvz48bO6O3KEI/mclpaGzWajZs0IWrRshYen51nb4tLM//MPbh01gs8//QhPT09atIyxF3P279tX7njFdZ1aF3v2uhKbrYhZM77DYvGgS5duZ7WNPnln/sIFf1F0spvAo0lJbN60EU9PTxo3bnrWOKVxM5/c9xcUltHSMV26dsdsNtvfn3B6d6QVder9D38vX2oftn/fXm675UZemFj8nrSLvR/XubVciPLk96kn29p16ET1sHDMbm4kpzi2zzgXs/nUcfA/OX7qiZueva4iIDAQm81m736oNAUFBaxds5pf5/5k3+5AcSEzLy8fNzd3PL28LmiepW2PAgODyM3NtT/lXJCfz9Gj/xz/51it3H7Ljdw35k7y8/KIqF2HocNHEBJS/P61rOyssn4mET2hIGe74sqr2bh+LWvXrOb/HhxHy5jWZGdlsX5dcZdGY8bej+Vkf+ohoaE0btKU7dvi8fb2pmVMa/t0WrdpR4uWrdi8aSOP/Pd+mrdoSVpaKls2b6JN23Y8PP4JZy3iOQ0fcRNbNm/i17k/sWvnDmrWrEV8fByJCUeoWq0a7dp3LHsiIi6ga7ce/DhnFosWziclJYXgkBDWn3zp0uDrhmJ2c+PGm27h+YlP887UN1m14m9yc3PZtHE9Pj4+DB1+Y4npJSQc4fHxD9G4cRO2bYvn+LFjtO/QkRo1S74/4c95v7PpZH+sp1StWo0bR93CqFtuY9pbb/LGq5Np3aYt3t4+bFi/jvz8PFrFtLG/CK9Tl64smP8n+fl59hepnnLTLbfx0vMT+fzTj9iwfi3BIaFs3riBtLQ0mjRtRv0GDSv7p5R/qbDwcAZfdz2zZ83g//47jpatil/4u2fPbvbv20uVqlXp2Kkz6WnFFxXXrlnFm6+9wnVDh9OiZStaxbRh44Z1PP7If6nfsBHxsVtJS0ul97X97C9HvuHGUbzy0iRee+VFWrdtjzU7i00bNxASGnrWug/FhYqrrunDn3/8xofvT+Ph8Y8DcPd/7uO5Zybw84+z2RYfR0REbfbs2c2+vXuo36Ah1w0dfta0Xpr0DK3bticl+SjxcbF4eXmVeDlieFgNdmZu54XnnqFDp070ubb/WdO4fvgI1q9by7dff8m2uDgsFgvr1q7B3d2dkaNGV+h31zZELgaTycSom2/jrf+9xouTniGmdVtSU48THxdLlSpV6Tvgn/ehhIXXYO+e3bz15mvExLRh2IiRtGzVitWrVvLMU48TUbsO8XFb7Re3808W28/FbDbTsFETNm/agMXiQePTukmLim7BjO+mA5QoKJQnVkeWe8zY+3n0/x5gzqwZtG7dtsLreM9eV/LTnFmsX7eWp54YT62I2mxcv65CBQWzm1uZ+RwRUZuQkFB27tzOyy9OwtPTky2bNwJQUJBf4unKM7Vt34Hvv/uGP377laTERAIDA9m4cT0AMW3aVmj5xbW1bd8BX18/srJO0KlzR3x8fc9q071HL/6a9zsb1hevwzVq1GTzpo3k5+cx6pbb8PL2dnh+Xt7eBAUFk5p6nJdffI5uPXrR+RwXOR0VFBxM8xat2LRxPY2bNKVa9dKfKnRUj15XMu/3X1m+dDEpyUepXj3M/n6GfgMGARdnP346nVtLZXAkv1u0asW8P37l26+/YNeO7SQlJdp71sjPL30/XZpT7xr7/NOPiIyK5o67xtCiZSsOHTzAlJdfoEmTpuzauYPDhw8BkFfKPK7tN4A1q1aw4u9l7Nu7h0aNm1BYVMSmDespLCygT9/+9t49KjrP0rZHLVvFsHjRAp57ZgKNGjdh184dJCYm2GPz8vamXfuOLFm8kCcefZjGTZtx5PAhjh1LoVGjJvj7ByBSFj2hIGcxmUz89+FHGX3bnQQEBrLy72Xs2L6Ndu078NwLLxMV3bxE+1NPJbRp277EnQwmk4n/e3QCg68r7q90+bKlHDp0kGv7DWDsuP9e0mVyRM1aETz3wiu0a9+RQwcPsGzpEnx8fBj34MM64BFDCQoO5tnnXixelw8dYM2qFVQPC2fcgw/Tf+BgAJo2i+TJZ56jSdOmbFi/lp07ttG6TTuee+Hls05kel1xFY0aNWbVyhWkp6XStVsP7v7PfWfNd/eunaz4e1mJf5tOnsR3696TRx57koaNGrN500bWrllNg4aNeOa5F+0XAgEiI6PtXSWc/sQTQFR0c56e+DzNW7Rkx/ZtrPx7OdXDwnj8yWd0IVAq3fARN/HIYxNo2iySuNitLFu6hNzcXPr2G8ikF6fg6+tHjZo1uW7oDbi7W4iN3WK/uPbf/3uUgYOuw5pjZcXypVgsFm665VZG33anffotW8Xw2IRnqFO3HmtXr2Lnju107NyViZMm2198eKaRo0ZTvXoYa9esYvGiBQDUrFmLF156lSuuvJpjx1JYumQxuTk5XD/sBp6e+IL9ROV0w24YyZ5dO9m1cyf16jfg8SefLdFFyajRt1GjRk327tllv7PpTOHhNZj4wmRiWrdhW3wsmzdtoFlkJE89+7y9W8Ty0jZELpZOXbry0COPU6NGLdasXsmhgwfp2q0HE194uUS+DR02gnr1G3D40EH7ifed94ylW/eepKQks2XTRtq0bU+3Hr0A7Cf2pWnStLhY0LhJkxLHyA0aNsLr5F2JjZuUzBdHY3VEterVuemW2ygsLGTaW2/anzYsL4vFwhNPPkvzFi05sH8/8bFb6TdgEJ6enhXqSqmsfPbw8GD8E08RGdWcbfGx7N+3lxEjb6ZGzVoUFhaWuChypsDAIJ6d9BLtO3Rkx/Zt/L18Kf7+Adxx93+46uRT2HJ5sVgsdO5afEH/9PcMndlmwtPPceXVvTmWksKqlX8TFBzMmHvvt19gL4877h5DlSpV2RYfd1b3hRVx6gmmyng6AYq7aXxq4gv06HkFRw4fZsXfy6hWvToPPfIYHTp1Bi7Ofvx0OreWyuBIfrdp257b7xqDn58/q1b+jdls5q4x9wJl76fP5dp+A4iMiiYl+SgH9xf3rHHDiJvoc21/rNnZrFu3hnr1GzBg0BAAjhw69zz8/Px47sVXGDZiJO4WCytXLGfj+nWEhYczZuw4bh59e6XM81zbo5tuuZWOnbpw4sQJ1q9bQ+MmTel0RuHzrnvGMnT4jRQUFLB08UKSk49y5dW9+b9HXevGX3FdJpue8RYRERG57I0bexcpycm8//EXJV7CKCJSlh3bt1G7Tl17IWT3rp08+fgjRETUPu9LJUXk/AoLC3ny8Uc4eGA/097/pNT+1EVERFyJujwSERERERGRc8rIyODFSc/g4+NL08gosNns3acMOUe3aiLimJkzvmXVir85dPAAV1x5tYoJIiJiGCooiIiIiIiIyDkFBATw+JPPMnvWDDauX4ubmzv1GzTguqHD7S+iF5Hy2793LynJR+nSrQe3nNYtooiIiKtTl0ciIiIiIiIiIiIiIlImvZRZRERERERERERERETKpIKCiIiIiIiIiIiIiIiUSQUFEREREREREREREREpkwoKIiIiIiIiIiIiIiJSJhUURERERERERERERESkTCooiIiIiIiIiIiIiIhImVRQEBERERERERERERGRMqmgICIiIiIiIiIiIiIiZVJBQUREREREREREREREyvT/j/vMygsEkYoAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "g = sns.PairGrid(Train2, hue=\"Output\")\n",
+ "g.map_diag(sns.histplot)\n",
+ "g.map_offdiag(sns.scatterplot)\n",
+ "g.add_legend()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 198,
+ "id": "2025369e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "svm._classes.SVC ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 1.00 0.94 1233\n",
+ " 1 1.00 0.36 0.53 237\n",
+ "\n",
+ " accuracy 0.90 1470\n",
+ " macro avg 0.95 0.68 0.74 1470\n",
+ "weighted avg 0.91 0.90 0.88 1470\n",
+ " \n",
+ "\n",
+ "linear_model._stochastic_gradient.SGDClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.91 0.95 0.93 1233\n",
+ " 1 0.64 0.49 0.56 237\n",
+ "\n",
+ " accuracy 0.87 1470\n",
+ " macro avg 0.77 0.72 0.74 1470\n",
+ "weighted avg 0.86 0.87 0.87 1470\n",
+ " \n",
+ "\n",
+ "linear_model._perceptron.Perceptron ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 0.99 0.99 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 0.99 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "naive_bayes.MultinomialNB ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 0.94 0.96 1233\n",
+ " 1 0.74 0.98 0.85 237\n",
+ "\n",
+ " accuracy 0.94 1470\n",
+ " macro avg 0.87 0.96 0.91 1470\n",
+ "weighted avg 0.96 0.94 0.95 1470\n",
+ " \n",
+ "\n",
+ "linear_model._passive_aggressive.PassiveAggressiveClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "naive_bayes.GaussianNB ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "gaussian_process._gpc.GaussianProcessClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.91 0.95 0.93 1233\n",
+ " 1 0.68 0.54 0.60 237\n",
+ "\n",
+ " accuracy 0.89 1470\n",
+ " macro avg 0.80 0.75 0.77 1470\n",
+ "weighted avg 0.88 0.89 0.88 1470\n",
+ " \n",
+ "\n",
+ "neighbors._classification.KNeighborsClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.90 0.99 0.94 1233\n",
+ " 1 0.90 0.43 0.59 237\n",
+ "\n",
+ " accuracy 0.90 1470\n",
+ " macro avg 0.90 0.71 0.77 1470\n",
+ "weighted avg 0.90 0.90 0.89 1470\n",
+ " \n",
+ "\n",
+ "ensemble._forest.RandomForestClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "ensemble._weight_boosting.AdaBoostClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "ensemble._forest.ExtraTreesClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "ensemble._gb.GradientBoostingClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "neural_network._multilayer_perceptron.MLPClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "discriminant_analysis.QuadraticDiscriminantAnalysis ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ "sklearn.XGBClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n",
+ ".core.CatBoostClassifier ShuffleSplit\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 1233\n",
+ " 1 1.00 1.00 1.00 237\n",
+ "\n",
+ " accuracy 1.00 1470\n",
+ " macro avg 1.00 1.00 1.00 1470\n",
+ "weighted avg 1.00 1.00 1.00 1470\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 198,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit\n",
+ "from catboost import CatBoostClassifier\n",
+ "from datetime import datetime\n",
+ "n_split=10\n",
+ "acc=[]\n",
+ "\n",
+ "X=Train\n",
+ "y=Y\n",
+ "# Xtrain, Xtest=pd.DataFrame(),pd.DataFrame()\n",
+ "\n",
+ "import time\n",
+ "# cvs=[ KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit]\n",
+ "cvs =[ShuffleSplit] \n",
+ "\n",
+ "def convert_time(sec):\n",
+ " sec = sec % (24 * 3600)\n",
+ " hour = sec // 3600\n",
+ " sec %= 3600\n",
+ " min = sec // 60\n",
+ " sec %= 60\n",
+ " return (\"%02d:%02d:%02d\" % (hour, min, sec) )\n",
+ "\n",
+ "def stratified_cv(X, y, clf_class, shuffle=True, **kwargs):\n",
+ " for cv in cvs:\n",
+ " stratified_k_fold = cv(n_splits=n_split).split(X,y)\n",
+ " y_pred = y.copy()\n",
+ " k=0\n",
+ " last=0\n",
+ " start=time.time()\n",
+ " for ii, jj in (stratified_k_fold): \n",
+ " curr=time.time()\n",
+ " k+=1\n",
+ " Xtrain, Xtest = X.iloc[ii], X.iloc[jj]\n",
+ " ytrain = y.iloc[ii]\n",
+ " clf = clf_class(**kwargs)\n",
+ " clf= clf.fit(Xtrain,ytrain)\n",
+ "# clf.grid_search(grid,X=Train,y=Y)\n",
+ " y_pred.iloc[jj] = clf.predict(Xtest)\n",
+ " last=time.time()\n",
+ " p=k*100/n_split\n",
+ " e=convert_time(last-curr)\n",
+ " u=convert_time(last-start)\n",
+ " t=u*n_split\n",
+ "# print(p,\"% percent completed......\",u ,\" \",datetime.now())\n",
+ "\n",
+ " print(str(clf_class)[16:-2],str(cv)[39:-2])\n",
+ " print(classification_report(y,y_pred ),\"\\n\")\n",
+ "\n",
+ " # print(classification_report(y, y_pred))\n",
+ "\n",
+ " return clf\n",
+ "\n",
+ "\n",
+ "stratified_cv(X, y, svm.SVC)\n",
+ "stratified_cv(X, y, SGDClassifier,max_iter=10)\n",
+ "stratified_cv(X, y, Perceptron)\n",
+ "stratified_cv(X, y, MultinomialNB,alpha=0.01)\n",
+ "stratified_cv(X, y, PassiveAggressiveClassifier)\n",
+ "stratified_cv(X, y,GaussianNB)\n",
+ "stratified_cv(X, y,GaussianProcessClassifier,1.0 * RBF(1.0))\n",
+ "stratified_cv(X, y,KNeighborsClassifier,n_neighbors=5)\n",
+ "stratified_cv(X, y,RandomForestClassifier)\n",
+ "stratified_cv(X, y,AdaBoostClassifier)\n",
+ "stratified_cv(X, y,ExtraTreesClassifier)\n",
+ "stratified_cv(X, y,GradientBoostingClassifier)\n",
+ "stratified_cv(X, y,MLPClassifier,alpha=1, max_iter=1000)\n",
+ "stratified_cv(X, y,QuadraticDiscriminantAnalysis)\n",
+ "stratified_cv(X, y,xgboost.XGBClassifier)\n",
+ "stratified_cv(X, y,cb.CatBoostClassifier,verbose=0,random_seed= 42,\n",
+ " depth=4, l2_leaf_reg= 4, iterations=800, learning_rate= 0.036)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 199,
+ "id": "a89a524d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'subsample': 0.8,\n",
+ " 'reg_lambda': 1.5,\n",
+ " 'reg_alpha': 2,\n",
+ " 'n_estimators': 300,\n",
+ " 'min_child_weight': 1,\n",
+ " 'max_depth': 5,\n",
+ " 'learning_rate': 1,\n",
+ " 'gamma': 2,\n",
+ " 'colsample_bytree': 0.6}"
+ ]
+ },
+ "execution_count": 199,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import RandomizedSearchCV\n",
+ "import random\n",
+ "\n",
+ "\n",
+ "# model_selection.GridSearchCV(estimator, …)\n",
+ "# model_selection.HalvingGridSearchCV(…[, …])\n",
+ "# model_selection.ParameterGrid(param_grid)\n",
+ "# model_selection.ParameterSampler(…[, …])\n",
+ "# model_selection.RandomizedSearchCV(…[, …])\n",
+ "# model_selection.HalvingRandomSearchCV(…[, …])\n",
+ "\n",
+ "param_dist = {\n",
+ " 'min_child_weight': [0.1, 0.5,1],\n",
+ " 'gamma': [0.5, 1, 1.5, 2, 5],\n",
+ " 'subsample': [1.2, 0.8, 1.0],\n",
+ " 'colsample_bytree': [0.6, 0.8, 1.0],\n",
+ " 'max_depth': [4,5,6],\n",
+ " 'learning_rate': [0.1, 1,1.5,3],\n",
+ " 'n_estimators': [350, 250, 300],\n",
+ " 'reg_alpha': [ 2, 1,3],\n",
+ " 'reg_lambda': [ 2, 1,1.5]\n",
+ "}\n",
+ "clf = xgboost.XGBClassifier()\n",
+ "rsh = RandomizedSearchCV(estimator=clf, param_distributions=param_dist)\n",
+ "rsh.fit(X, y)\n",
+ "rsh.best_params_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 200,
+ "id": "6e0e7e42",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1470, 33, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Train /= np.min(Train) # Normalise data to [0, 1] range\n",
+ "# Test /= np.max(Test) \n",
+ "\n",
+ "Train=np.array(Train)\n",
+ "Y=np.array(Y)\n",
+ "\n",
+ "Train = np.reshape(Train,( Train.shape[0],Train.shape[1], 1 ))\n",
+ "print(Train.shape)\n",
+ "\n",
+ "# define dataloader parameters\n",
+ "batch_size = 64\n",
+ "num_workers=0\n",
+ "train_tensor = torch.utils.data.TensorDataset(torch.Tensor(Train),torch.Tensor(Y)) \n",
+ "train_loader = torch.utils.data.DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)\n",
+ "\n",
+ "train_x, test_x, train_y, test_y = train_test_split(Train, Y,test_size=0.2, random_state=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 201,
+ "id": "5484955d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_4\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv1d_20 (Conv1D) (None, 32, 64) 192 \n",
+ "_________________________________________________________________\n",
+ "conv1d_21 (Conv1D) (None, 31, 32) 4128 \n",
+ "_________________________________________________________________\n",
+ "conv1d_22 (Conv1D) (None, 30, 16) 1040 \n",
+ "_________________________________________________________________\n",
+ "conv1d_23 (Conv1D) (None, 29, 8) 264 \n",
+ "_________________________________________________________________\n",
+ "conv1d_24 (Conv1D) (None, 28, 4) 68 \n",
+ "_________________________________________________________________\n",
+ "flatten_4 (Flatten) (None, 112) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_8 (Dense) (None, 2) 226 \n",
+ "_________________________________________________________________\n",
+ "dense_9 (Dense) (None, 1) 3 \n",
+ "=================================================================\n",
+ "Total params: 5,921\n",
+ "Trainable params: 5,921\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Conv1D, Flatten\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# clear_session()\n",
+ "\n",
+ "model = Sequential()\n",
+ "model.add(Conv1D(64, 2, activation=\"relu\", input_shape=(Train.shape[1], 1)))\n",
+ "# model.add(Flatten())\n",
+ "model.add(Conv1D(32, 2, activation=\"relu\"))\n",
+ "model.add(Conv1D(16, 2, activation=\"relu\"))\n",
+ "model.add(Conv1D(8, 2, activation=\"relu\"))\n",
+ "model.add(Conv1D(4, 2, activation=\"relu\"))\n",
+ "model.add(Flatten())\n",
+ "model.add(Dense(2, activation=\"softmax\"))\n",
+ "model.add(Dense(1))\n",
+ "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 202,
+ "id": "d32c9c97",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 0s 4ms/step - loss: 0.1900\n",
+ "0.174648752017897\n",
+ "MSE: 0.1746\n",
+ "SCORE= 99.58209001882484 %\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# from keras.backend import clear_session\n",
+ "\n",
+ "pred_y = model.predict(test_x)\n",
+ "\n",
+ "print(model.evaluate(test_x,test_y))\n",
+ " \n",
+ "print(\"MSE: %.4f\" % mean_squared_error(test_y, pred_y))\n",
+ "\n",
+ "score = max(0, 100-np.sqrt(mean_squared_error(test_y,pred_y)))\n",
+ "print(\"SCORE=\",score,\"%\\n\")\n",
+ "\n",
+ "# clear_session()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a5e7bb9",
+ "metadata": {},
+ "source": [
+ "# Key Features\n",
+ " ## TOP 14\n",
+ " 1 'OverTime'\n",
+ " 2 'JobLevel'\n",
+ " 3 'StockOptionLevel'\n",
+ " 4 'MonthlyIncome'\n",
+ " 5 'TotalWorkingYears'\n",
+ " 6 'MaritalStatus'\n",
+ " 7 'YearsWithCurrManager'\n",
+ " 8 'JobRole'\n",
+ " 9 'JobInvolvement'\n",
+ " 10 'EnvironmentSatisfaction'\n",
+ " 11 'Department'\n",
+ " 12 'YearsInCurrentRole'\n",
+ " 13 'NumCompaniesWorked'\n",
+ " 14 'Age'\n",
+ " \n",
+ " ## LESS Priority\n",
+ " 15 'BusinessTravel'\n",
+ " 16 'WorkLifeBalance'\n",
+ " 17 'JobSatisfaction'\n",
+ " 18 'DistanceFromHome'\n",
+ " 19 'PerformanceRating'\n",
+ " 20 'YearsSinceLastPromotion'\n",
+ " 21 'HourlyRate'\n",
+ " 22 'DailyRate' \n",
+ " 23 'RelationshipSatisfaction'\n",
+ " 24 'PercentSalaryHike'\n",
+ " 25 'TrainingTimesLastYear'\n",
+ " 26 'MonthlyRate'\n",
+ " 27 'Education',\n",
+ " 26 'EducationField'\n",
+ " 27 'Gender'\n",
+ " 28 'EmployeeCount'\n",
+ " 29 'StandardHours'\n",
+ " 30 'Over18'"
+ ]
+ }
+ ],
+ "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": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/007/solution/IBM_HR_Employee_Attrition_Profil.html b/007/solution/IBM_HR_Employee_Attrition_Profil.html
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@@ -0,0 +1,17729 @@
+IBM_HR_Employee_Attrition_Report Dataset statistics
Number of variables 34 Number of observations 1470 Missing cells 0 Missing cells (%) 0.0% Total size in memory 1.0 MiB Average record size in memory 738.8 B
Reproduction
Analysis started 2021-08-29 02:40:42.830974 Analysis finished 2021-08-29 02:40:43.993418 Duration 1.16 second Software version pandas-profiling v3.0.0 Download configuration config.json
Age Real number (ℝ≥0 )
Distinct 43 Distinct (%) 2.9% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 36.92380952
Minimum 18 Maximum 60 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:44.201418 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 18 5-th percentile 24 Q1 30 median 36 Q3 43 95-th percentile 54 Maximum 60 Range 42 Interquartile range (IQR) 13
Descriptive statistics
Standard deviation 9.135373489 Coefficient of variation (CV) 0.2474114564 Kurtosis -0.4041451372 Mean 36.92380952 Median Absolute Deviation (MAD) 6 Skewness 0.4132863019 Sum 54278 Variance 83.45504879 Monotonicity Not monotonic
2021-08-29T08:10:44.446519 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=43)
Value Count Frequency (%) 35 78
5.3% 34 77
5.2% 36 69
4.7% 31 69
4.7% 29 68
4.6% 32 61
4.1% 30 60
4.1% 33 58
3.9% 38 58
3.9% 40 57
3.9% Other values (33) 815 55.4%
Value Count Frequency (%) 18 8
0.5% 19 9
0.6% 20 11
0.7% 21 13
0.9% 22 16
1.1% 23 14
1.0% 24 26 1.8%
25 26 1.8%
26 39 2.7%
27 48 3.3%
Value Count Frequency (%) 60 5
0.3% 59 10 0.7%
58 14 1.0%
57 4
0.3% 56 14 1.0%
55 22 1.5%
54 18 1.2%
53 19 1.3%
52 18 1.2%
51 19 1.3%
Distinct 2 Distinct (%) 0.1% Missing 0 Missing (%) 0.0% Memory size 85.1 KiB
Toggle details
Characters and Unicode
Total characters 3177 Distinct characters 5 Distinct categories 2 ? Distinct scripts 1 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Yes 2nd row No 3rd row Yes 4th row No 5th row No
Common Values Value Count Frequency (%) No 1233 83.9%
Yes 237
16.1%
Value Count Frequency (%) no 1233 83.9%
yes 237
16.1%
Most occurring characters Value Count Frequency (%) N 1233 38.8%
o 1233 38.8%
Y 237
7.5% e 237
7.5% s 237
7.5%
Most occurring categories Value Count Frequency (%) Lowercase Letter 1707 53.7%
Uppercase Letter 1470 46.3%
Most frequent character per category Lowercase Letter Value Count Frequency (%) o 1233 72.2%
e 237
13.9% s 237
13.9%
Uppercase Letter Value Count Frequency (%) N 1233 83.9%
Y 237
16.1%
Most occurring scripts Value Count Frequency (%) Latin 3177 100.0%
Most frequent character per script Latin Value Count Frequency (%) N 1233 38.8%
o 1233 38.8%
Y 237
7.5% e 237
7.5% s 237
7.5%
Most occurring blocks Value Count Frequency (%) ASCII 3177 100.0%
Most frequent character per block ASCII Value Count Frequency (%) N 1233 38.8%
o 1233 38.8%
Y 237
7.5% e 237
7.5% s 237
7.5%
Distinct 3 Distinct (%) 0.2% Missing 0 Missing (%) 0.0% Memory size 101.3 KiB
Travel_Rarely 1043
Travel_Frequently 277
Non-Travel 150
Toggle details
Characters and Unicode
Total characters 19768 Distinct characters 17 Distinct categories 4 ? Distinct scripts 2 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Travel_Rarely 2nd row Travel_Frequently 3rd row Travel_Rarely 4th row Travel_Frequently 5th row Travel_Rarely
Common Values Value Count Frequency (%) Travel_Rarely 1043 71.0%
Travel_Frequently 277
18.8% Non-Travel 150
10.2%
Value Count Frequency (%) travel_rarely 1043 71.0%
travel_frequently 277
18.8% non-travel 150
10.2%
Most occurring characters Value Count Frequency (%) e 3067 15.5%
r 2790 14.1%
l 2790 14.1%
a 2513 12.7%
T 1470 7.4%
v 1470 7.4%
_ 1320 6.7%
y 1320 6.7%
R 1043
5.3% n 427
2.2% Other values (7) 1558 7.9%
Most occurring categories Value Count Frequency (%) Lowercase Letter 15358 77.7%
Uppercase Letter 2940
14.9% Connector Punctuation 1320
6.7% Dash Punctuation 150
0.8%
Most frequent character per category Lowercase Letter Value Count Frequency (%) e 3067 20.0%
r 2790 18.2%
l 2790 18.2%
a 2513 16.4%
v 1470 9.6%
y 1320 8.6%
n 427
2.8% q 277
1.8% u 277
1.8% t 277
1.8%
Uppercase Letter Value Count Frequency (%) T 1470 50.0%
R 1043 35.5%
F 277
9.4% N 150
5.1%
Connector Punctuation Value Count Frequency (%) _ 1320 100.0%
Dash Punctuation Value Count Frequency (%) - 150 100.0%
Most occurring scripts Value Count Frequency (%) Latin 18298 92.6%
Common 1470
7.4%
Most frequent character per script Latin Value Count Frequency (%) e 3067 16.8%
r 2790 15.2%
l 2790 15.2%
a 2513 13.7%
T 1470 8.0%
v 1470 8.0%
y 1320 7.2%
R 1043
5.7% n 427
2.3% F 277
1.5% Other values (5) 1131
6.2%
Common Value Count Frequency (%) _ 1320 89.8%
- 150
10.2%
Most occurring blocks Value Count Frequency (%) ASCII 19768 100.0%
Most frequent character per block ASCII Value Count Frequency (%) e 3067 15.5%
r 2790 14.1%
l 2790 14.1%
a 2513 12.7%
T 1470 7.4%
v 1470 7.4%
_ 1320 6.7%
y 1320 6.7%
R 1043
5.3% n 427
2.2% Other values (7) 1558 7.9%
Distinct 886 Distinct (%) 60.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 802.4857143
Minimum 102 Maximum 1499 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:45.232543 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 102 5-th percentile 165.35 Q1 465 median 802 Q3 1157 95-th percentile 1424.1 Maximum 1499 Range 1397 Interquartile range (IQR) 692
Descriptive statistics
Standard deviation 403.5090999 Coefficient of variation (CV) 0.5028240288 Kurtosis -1.203822808 Mean 802.4857143 Median Absolute Deviation (MAD) 344 Skewness -0.003518568352 Sum 1179654 Variance 162819.5937 Monotonicity Not monotonic
2021-08-29T08:10:45.541061 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=50)
Value Count Frequency (%) 691 6
0.4% 408 5
0.3% 530 5
0.3% 1329 5
0.3% 1082 5
0.3% 329 5
0.3% 829 4
0.3% 1469 4
0.3% 267 4
0.3% 217 4
0.3% Other values (876) 1423 96.8%
Value Count Frequency (%) 102 1
0.1% 103 1
0.1% 104 1
0.1% 105 1
0.1% 106 1
0.1% 107 1
0.1% 109 1
0.1% 111 3 0.2%
115 1
0.1% 116 2 0.1%
Value Count Frequency (%) 1499 1
0.1% 1498 1
0.1% 1496 2 0.1%
1495 3 0.2%
1492 1
0.1% 1490 4 0.3%
1488 1
0.1% 1485 3 0.2%
1482 1
0.1% 1480 2 0.1%
Distinct 3 Distinct (%) 0.2% Missing 0 Missing (%) 0.0% Memory size 105.7 KiB
Research & Development 961
Sales 446
Human Resources
63
Toggle details
Characters and Unicode
Total characters 24317 Distinct characters 20 Distinct categories 4 ? Distinct scripts 2 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Sales 2nd row Research & Development 3rd row Research & Development 4th row Research & Development 5th row Research & Development
Common Values Value Count Frequency (%) Research & Development 961 65.4%
Sales 446 30.3%
Human Resources 63
4.3%
Value Count Frequency (%) research 961 27.8%
961 27.8%
development 961 27.8%
sales 446 12.9%
human 63
1.8% resources 63
1.8%
Most occurring characters Value Count Frequency (%) e 5377 22.1%
1985
8.2% s 1533
6.3% a 1470
6.0% l 1407
5.8% R 1024
4.2% r 1024
4.2% c 1024
4.2% o 1024
4.2% m 1024
4.2% Other values (10) 7425 30.5%
Most occurring categories Value Count Frequency (%) Lowercase Letter 18877 77.6%
Uppercase Letter 2494
10.3% Space Separator 1985
8.2% Other Punctuation 961
4.0%
Most frequent character per category Lowercase Letter Value Count Frequency (%) e 5377 28.5%
s 1533
8.1% a 1470
7.8% l 1407
7.5% r 1024
5.4% c 1024
5.4% o 1024
5.4% m 1024
5.4% n 1024
5.4% h 961
5.1% Other values (4) 3009 15.9%
Uppercase Letter Value Count Frequency (%) R 1024 41.1%
D 961 38.5%
S 446 17.9%
H 63
2.5%
Space Separator Value Count Frequency (%) 1985 100.0%
Other Punctuation Value Count Frequency (%) & 961 100.0%
Most occurring scripts Value Count Frequency (%) Latin 21371 87.9%
Common 2946
12.1%
Most frequent character per script Latin Value Count Frequency (%) e 5377 25.2%
s 1533
7.2% a 1470
6.9% l 1407
6.6% R 1024
4.8% r 1024
4.8% c 1024
4.8% o 1024
4.8% m 1024
4.8% n 1024
4.8% Other values (8) 5440 25.5%
Common Value Count Frequency (%) 1985 67.4%
& 961 32.6%
Most occurring blocks Value Count Frequency (%) ASCII 24317 100.0%
Most frequent character per block ASCII Value Count Frequency (%) e 5377 22.1%
1985
8.2% s 1533
6.3% a 1470
6.0% l 1407
5.8% R 1024
4.2% r 1024
4.2% c 1024
4.2% o 1024
4.2% m 1024
4.2% Other values (10) 7425 30.5%
Distinct 29 Distinct (%) 2.0% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 9.192517007
Minimum 1 Maximum 29 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:46.121162 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 7 Q3 14 95-th percentile 26 Maximum 29 Range 28 Interquartile range (IQR) 12
Descriptive statistics
Standard deviation 8.106864436 Coefficient of variation (CV) 0.8818982254 Kurtosis -0.2248334049 Mean 9.192517007 Median Absolute Deviation (MAD) 5 Skewness 0.9581179957 Sum 13513 Variance 65.72125098 Monotonicity Not monotonic
2021-08-29T08:10:46.307613 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=29)
Value Count Frequency (%) 2 211 14.4%
1 208 14.1%
10 86
5.9% 9 85
5.8% 3 84
5.7% 7 84
5.7% 8 80
5.4% 5 65
4.4% 4 64
4.4% 6 59
4.0% Other values (19) 444 30.2%
Value Count Frequency (%) 1 208 14.1%
2 211 14.4%
3 84
5.7% 4 64
4.4% 5 65
4.4% 6 59
4.0% 7 84
5.7% 8 80
5.4% 9 85 5.8%
10 86 5.9%
Value Count Frequency (%) 29 27 1.8%
28 23 1.6%
27 12 0.8%
26 25 1.7%
25 25 1.7%
24 28 1.9%
23 27 1.8%
22 19 1.3%
21 18 1.2%
20 25 1.7%
Distinct 5 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.91292517
Minimum 1 Maximum 5 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:46.566451 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 3 Q3 4 95-th percentile 4 Maximum 5 Range 4 Interquartile range (IQR) 2
Descriptive statistics
Standard deviation 1.024164945 Coefficient of variation (CV) 0.3515932902 Kurtosis -0.5591149664 Mean 2.91292517 Median Absolute Deviation (MAD) 1 Skewness -0.289681082 Sum 4282 Variance 1.048913834 Monotonicity Not monotonic
2021-08-29T08:10:46.770969 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=5)
Value Count Frequency (%) 3 572 38.9%
4 398 27.1%
2 282 19.2%
1 170
11.6% 5 48
3.3%
Value Count Frequency (%) 1 170
11.6% 2 282 19.2%
3 572 38.9%
4 398 27.1%
5 48
3.3%
Value Count Frequency (%) 5 48
3.3% 4 398 27.1%
3 572 38.9%
2 282 19.2%
1 170
11.6%
Distinct 6 Distinct (%) 0.4% Missing 0 Missing (%) 0.0% Memory size 97.1 KiB
Life Sciences 606
Medical 464
Marketing 159
Technical Degree 132
Other 82
Toggle details
Characters and Unicode
Total characters 15484 Distinct characters 26 Distinct categories 3 ? Distinct scripts 2 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Life Sciences 2nd row Life Sciences 3rd row Other 4th row Life Sciences 5th row Medical
Common Values Value Count Frequency (%) Life Sciences 606 41.2%
Medical 464 31.6%
Marketing 159
10.8% Technical Degree 132
9.0% Other 82
5.6% Human Resources 27
1.8%
Value Count Frequency (%) life 606 27.1%
sciences 606 27.1%
medical 464 20.8%
marketing 159
7.1% technical 132
5.9% degree 132
5.9% other 82
3.7% human 27
1.2% resources 27
1.2%
Most occurring characters Value Count Frequency (%) e 3105 20.1%
i 1967 12.7%
c 1967 12.7%
n 924
6.0% a 782
5.1% 765
4.9% s 660
4.3% M 623
4.0% L 606
3.9% f 606
3.9% Other values (16) 3479 22.5%
Most occurring categories Value Count Frequency (%) Lowercase Letter 12484 80.6%
Uppercase Letter 2235
14.4% Space Separator 765
4.9%
Most frequent character per category Lowercase Letter Value Count Frequency (%) e 3105 24.9%
i 1967 15.8%
c 1967 15.8%
n 924
7.4% a 782
6.3% s 660
5.3% f 606
4.9% l 596
4.8% d 464
3.7% r 400
3.2% Other values (7) 1013
8.1%
Uppercase Letter Value Count Frequency (%) M 623 27.9%
L 606 27.1%
S 606 27.1%
T 132
5.9% D 132
5.9% O 82
3.7% H 27
1.2% R 27
1.2%
Space Separator Value Count Frequency (%) 765 100.0%
Most occurring scripts Value Count Frequency (%) Latin 14719 95.1%
Common 765
4.9%
Most frequent character per script Latin Value Count Frequency (%) e 3105 21.1%
i 1967 13.4%
c 1967 13.4%
n 924
6.3% a 782
5.3% s 660
4.5% M 623
4.2% L 606
4.1% f 606
4.1% S 606
4.1% Other values (15) 2873 19.5%
Common Value Count Frequency (%) 765 100.0%
Most occurring blocks Value Count Frequency (%) ASCII 15484 100.0%
Most frequent character per block ASCII Value Count Frequency (%) e 3105 20.1%
i 1967 12.7%
c 1967 12.7%
n 924
6.0% a 782
5.1% 765
4.9% s 660
4.3% M 623
4.0% L 606
3.9% f 606
3.9% Other values (16) 3479 22.5%
Distinct 1 Distinct (%) 0.1% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 1
Minimum 1 Maximum 1 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:47.172364 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 1 median 1 Q3 1 95-th percentile 1 Maximum 1 Range 0 Interquartile range (IQR) 0
Descriptive statistics
Standard deviation 0 Coefficient of variation (CV) 0 Kurtosis 0 Mean 1 Median Absolute Deviation (MAD) 0 Skewness 0 Sum 1470 Variance 0 Monotonicity Increasing
2021-08-29T08:10:47.497000 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=1)
Value Count Frequency (%) 1 1470 100.0%
Value Count Frequency (%) 1 1470 100.0%
Value Count Frequency (%) 1 1470 100.0%
Distinct 1470 Distinct (%) 100.0% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 1024.865306
Minimum 1 Maximum 2068 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:47.753078 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 96.45 Q1 491.25 median 1020.5 Q3 1555.75 95-th percentile 1967.55 Maximum 2068 Range 2067 Interquartile range (IQR) 1064.5
Descriptive statistics
Standard deviation 602.0243348 Coefficient of variation (CV) 0.5874180063 Kurtosis -1.223178906 Mean 1024.865306 Median Absolute Deviation (MAD) 533.5 Skewness 0.01657401958 Sum 1506552 Variance 362433.2997 Monotonicity Strictly increasing
2021-08-29T08:10:47.982239 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=50)
Value Count Frequency (%) 1 1
0.1% 1391 1
0.1% 1389 1
0.1% 1387 1
0.1% 1383 1
0.1% 1382 1
0.1% 1380 1
0.1% 1379 1
0.1% 1377 1
0.1% 1375 1
0.1% Other values (1460) 1460 99.3%
Value Count Frequency (%) 1 1 0.1%
2 1 0.1%
4 1 0.1%
5 1 0.1%
7 1 0.1%
8 1 0.1%
10 1 0.1%
11 1 0.1%
12 1 0.1%
13 1 0.1%
Value Count Frequency (%) 2068 1 0.1%
2065 1 0.1%
2064 1 0.1%
2062 1 0.1%
2061 1 0.1%
2060 1 0.1%
2057 1 0.1%
2056 1 0.1%
2055 1 0.1%
2054 1 0.1%
Distinct 4 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.721768707
Minimum 1 Maximum 4 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:48.245258 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 3 Q3 4 95-th percentile 4 Maximum 4 Range 3 Interquartile range (IQR) 2
Descriptive statistics
Standard deviation 1.093082215 Coefficient of variation (CV) 0.4016073121 Kurtosis -1.202520522 Mean 2.721768707 Median Absolute Deviation (MAD) 1 Skewness -0.3216544477 Sum 4001 Variance 1.194828728 Monotonicity Not monotonic
2021-08-29T08:10:48.383258 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=4)
Value Count Frequency (%) 3 453 30.8%
4 446 30.3%
2 287 19.5%
1 284 19.3%
Value Count Frequency (%) 1 284 19.3%
2 287 19.5%
3 453 30.8%
4 446 30.3%
Value Count Frequency (%) 4 446 30.3%
3 453 30.8%
2 287 19.5%
1 284 19.3%
Distinct 2 Distinct (%) 0.1% Missing 0 Missing (%) 0.0% Memory size 88.8 KiB
Toggle details
Characters and Unicode
Total characters 7056 Distinct characters 6 Distinct categories 2 ? Distinct scripts 1 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Female 2nd row Male 3rd row Male 4th row Female 5th row Male
Common Values Value Count Frequency (%) Male 882 60.0%
Female 588 40.0%
Value Count Frequency (%) male 882 60.0%
female 588 40.0%
Most occurring characters Value Count Frequency (%) e 2058 29.2%
a 1470 20.8%
l 1470 20.8%
M 882 12.5%
F 588
8.3% m 588
8.3%
Most occurring categories Value Count Frequency (%) Lowercase Letter 5586 79.2%
Uppercase Letter 1470
20.8%
Most frequent character per category Lowercase Letter Value Count Frequency (%) e 2058 36.8%
a 1470 26.3%
l 1470 26.3%
m 588
10.5%
Uppercase Letter Value Count Frequency (%) M 882 60.0%
F 588 40.0%
Most occurring scripts Value Count Frequency (%) Latin 7056 100.0%
Most frequent character per script Latin Value Count Frequency (%) e 2058 29.2%
a 1470 20.8%
l 1470 20.8%
M 882 12.5%
F 588
8.3% m 588
8.3%
Most occurring blocks Value Count Frequency (%) ASCII 7056 100.0%
Most frequent character per block ASCII Value Count Frequency (%) e 2058 29.2%
a 1470 20.8%
l 1470 20.8%
M 882 12.5%
F 588
8.3% m 588
8.3%
Distinct 71 Distinct (%) 4.8% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 65.89115646
Minimum 30 Maximum 100 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:48.732179 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 30 5-th percentile 33 Q1 48 median 66 Q3 83.75 95-th percentile 97 Maximum 100 Range 70 Interquartile range (IQR) 35.75
Descriptive statistics
Standard deviation 20.32942759 Coefficient of variation (CV) 0.3085304415 Kurtosis -1.196398456 Mean 65.89115646 Median Absolute Deviation (MAD) 18 Skewness -0.0323109529 Sum 96860 Variance 413.2856263 Monotonicity Not monotonic
2021-08-29T08:10:49.090399 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=50)
Value Count Frequency (%) 66 29
2.0% 98 28
1.9% 42 28
1.9% 48 28
1.9% 84 28
1.9% 57 27
1.8% 79 27
1.8% 96 27
1.8% 54 26
1.8% 52 26
1.8% Other values (61) 1196 81.4%
Value Count Frequency (%) 30 19 1.3%
31 15 1.0%
32 24 1.6%
33 19 1.3%
34 12 0.8%
35 18 1.2%
36 18 1.2%
37 18 1.2%
38 13 0.9%
39 17 1.2%
Value Count Frequency (%) 100 19 1.3%
99 20 1.4%
98 28 1.9%
97 21 1.4%
96 27 1.8%
95 23 1.6%
94 22 1.5%
93 16 1.1%
92 25 1.7%
91 18 1.2%
Distinct 4 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.729931973
Minimum 1 Maximum 4 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:49.358543 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 3 Q3 3 95-th percentile 4 Maximum 4 Range 3 Interquartile range (IQR) 1
Descriptive statistics
Standard deviation 0.711561143 Coefficient of variation (CV) 0.2606516023 Kurtosis 0.2709987665 Mean 2.729931973 Median Absolute Deviation (MAD) 0 Skewness -0.498419364 Sum 4013 Variance 0.5063192602 Monotonicity Not monotonic
2021-08-29T08:10:49.521486 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=4)
Value Count Frequency (%) 3 868 59.0%
2 375 25.5%
4 144
9.8% 1 83
5.6%
Value Count Frequency (%) 1 83
5.6% 2 375 25.5%
3 868 59.0%
4 144
9.8%
Value Count Frequency (%) 4 144
9.8% 3 868 59.0%
2 375 25.5%
1 83
5.6%
Distinct 5 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.063945578
Minimum 1 Maximum 5 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:49.689501 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 1 median 2 Q3 3 95-th percentile 4 Maximum 5 Range 4 Interquartile range (IQR) 2
Descriptive statistics
Standard deviation 1.106939899 Coefficient of variation (CV) 0.5363222319 Kurtosis 0.3991520554 Mean 2.063945578 Median Absolute Deviation (MAD) 1 Skewness 1.025401283 Sum 3034 Variance 1.22531594 Monotonicity Not monotonic
2021-08-29T08:10:49.849517 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=5)
Value Count Frequency (%) 1 543 36.9%
2 534 36.3%
3 218 14.8%
4 106
7.2% 5 69
4.7%
Value Count Frequency (%) 1 543 36.9%
2 534 36.3%
3 218 14.8%
4 106
7.2% 5 69
4.7%
Value Count Frequency (%) 5 69
4.7% 4 106
7.2% 3 218 14.8%
2 534 36.3%
1 543 36.9%
Distinct 9 Distinct (%) 0.6% Missing 0 Missing (%) 0.0% Memory size 107.9 KiB
Sales Executive 326
Research Scientist 292
Laboratory Technician 259
Manufacturing Director 145
Healthcare Representative 131
Other values (4) 317
Toggle details
Characters and Unicode
Total characters 26564 Distinct characters 29 Distinct categories 3 ? Distinct scripts 2 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Sales Executive 2nd row Research Scientist 3rd row Laboratory Technician 4th row Research Scientist 5th row Laboratory Technician
Common Values Value Count Frequency (%) Sales Executive 326 22.2%
Research Scientist 292 19.9%
Laboratory Technician 259 17.6%
Manufacturing Director 145 9.9%
Healthcare Representative 131 8.9%
Manager 102
6.9% Sales Representative 83
5.6% Research Director 80
5.4% Human Resources 52
3.5%
Value Count Frequency (%) sales 409 14.4%
research 372 13.1%
executive 326 11.5%
scientist 292 10.3%
laboratory 259 9.1%
technician 259 9.1%
director 225 7.9%
representative 214 7.5%
manufacturing 145
5.1% healthcare 131
4.6% Other values (3) 206 7.3%
Most occurring characters Value Count Frequency (%) e 3905 14.7%
a 2580
9.7% t 2098
7.9% c 2061
7.8% i 2012
7.6% r 1984
7.5% n 1468
5.5% s 1391
5.2% 1368
5.1% o 795
3.0% Other values (19) 6902 26.0%
Most occurring categories Value Count Frequency (%) Lowercase Letter 22358 84.2%
Uppercase Letter 2838
10.7% Space Separator 1368
5.1%
Most frequent character per category Lowercase Letter Value Count Frequency (%) e 3905 17.5%
a 2580 11.5%
t 2098 9.4%
c 2061 9.2%
i 2012 9.0%
r 1984 8.9%
n 1468
6.6% s 1391
6.2% o 795
3.6% h 762
3.4% Other values (10) 3302 14.8%
Uppercase Letter Value Count Frequency (%) S 701 24.7%
R 638 22.5%
E 326 11.5%
L 259
9.1% T 259
9.1% M 247
8.7% D 225
7.9% H 183
6.4%
Space Separator Value Count Frequency (%) 1368 100.0%
Most occurring scripts Value Count Frequency (%) Latin 25196 94.9%
Common 1368
5.1%
Most frequent character per script Latin Value Count Frequency (%) e 3905 15.5%
a 2580 10.2%
t 2098
8.3% c 2061
8.2% i 2012
8.0% r 1984
7.9% n 1468
5.8% s 1391
5.5% o 795
3.2% h 762
3.0% Other values (18) 6140 24.4%
Common Value Count Frequency (%) 1368 100.0%
Most occurring blocks Value Count Frequency (%) ASCII 26564 100.0%
Most frequent character per block ASCII Value Count Frequency (%) e 3905 14.7%
a 2580
9.7% t 2098
7.9% c 2061
7.8% i 2012
7.6% r 1984
7.5% n 1468
5.5% s 1391
5.2% 1368
5.1% o 795
3.0% Other values (19) 6902 26.0%
Distinct 4 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.728571429
Minimum 1 Maximum 4 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:50.238626 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 3 Q3 4 95-th percentile 4 Maximum 4 Range 3 Interquartile range (IQR) 2
Descriptive statistics
Standard deviation 1.102846123 Coefficient of variation (CV) 0.404184443 Kurtosis -1.222192568 Mean 2.728571429 Median Absolute Deviation (MAD) 1 Skewness -0.3296719587 Sum 4011 Variance 1.216269571 Monotonicity Not monotonic
2021-08-29T08:10:50.410607 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=4)
Value Count Frequency (%) 4 459 31.2%
3 442 30.1%
1 289 19.7%
2 280 19.0%
Value Count Frequency (%) 1 289 19.7%
2 280 19.0%
3 442 30.1%
4 459 31.2%
Value Count Frequency (%) 4 459 31.2%
3 442 30.1%
2 280 19.0%
1 289 19.7%
Distinct 3 Distinct (%) 0.2% Missing 0 Missing (%) 0.0% Memory size 91.9 KiB
Married 673
Single 470
Divorced 327
Toggle details
Characters and Unicode
Total characters 10147 Distinct characters 14 Distinct categories 2 ? Distinct scripts 1 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Single 2nd row Married 3rd row Single 4th row Married 5th row Married
Common Values Value Count Frequency (%) Married 673 45.8%
Single 470 32.0%
Divorced 327 22.2%
Value Count Frequency (%) married 673 45.8%
single 470 32.0%
divorced 327 22.2%
Most occurring characters Value Count Frequency (%) r 1673 16.5%
i 1470 14.5%
e 1470 14.5%
d 1000 9.9%
M 673 6.6%
a 673 6.6%
S 470
4.6% n 470
4.6% g 470
4.6% l 470
4.6% Other values (4) 1308 12.9%
Most occurring categories Value Count Frequency (%) Lowercase Letter 8677 85.5%
Uppercase Letter 1470
14.5%
Most frequent character per category Lowercase Letter Value Count Frequency (%) r 1673 19.3%
i 1470 16.9%
e 1470 16.9%
d 1000 11.5%
a 673 7.8%
n 470
5.4% g 470
5.4% l 470
5.4% v 327
3.8% o 327
3.8%
Uppercase Letter Value Count Frequency (%) M 673 45.8%
S 470 32.0%
D 327 22.2%
Most occurring scripts Value Count Frequency (%) Latin 10147 100.0%
Most frequent character per script Latin Value Count Frequency (%) r 1673 16.5%
i 1470 14.5%
e 1470 14.5%
d 1000 9.9%
M 673 6.6%
a 673 6.6%
S 470
4.6% n 470
4.6% g 470
4.6% l 470
4.6% Other values (4) 1308 12.9%
Most occurring blocks Value Count Frequency (%) ASCII 10147 100.0%
Most frequent character per block ASCII Value Count Frequency (%) r 1673 16.5%
i 1470 14.5%
e 1470 14.5%
d 1000 9.9%
M 673 6.6%
a 673 6.6%
S 470
4.6% n 470
4.6% g 470
4.6% l 470
4.6% Other values (4) 1308 12.9%
Distinct 1349 Distinct (%) 91.8% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 6502.931293
Minimum 1009 Maximum 19999 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:50.821279 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1009 5-th percentile 2097.9 Q1 2911 median 4919 Q3 8379 95-th percentile 17821.35 Maximum 19999 Range 18990 Interquartile range (IQR) 5468
Descriptive statistics
Standard deviation 4707.956783 Coefficient of variation (CV) 0.7239745541 Kurtosis 1.005232691 Mean 6502.931293 Median Absolute Deviation (MAD) 2199 Skewness 1.369816681 Sum 9559309 Variance 22164857.07 Monotonicity Not monotonic
2021-08-29T08:10:51.039049 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=50)
Value Count Frequency (%) 2342 4
0.3% 6142 3
0.2% 2741 3
0.2% 2559 3
0.2% 2610 3
0.2% 2451 3
0.2% 5562 3
0.2% 3452 3
0.2% 2380 3
0.2% 6347 3
0.2% Other values (1339) 1439 97.9%
Value Count Frequency (%) 1009 1 0.1%
1051 1 0.1%
1052 1 0.1%
1081 1 0.1%
1091 1 0.1%
1102 1 0.1%
1118 1 0.1%
1129 1 0.1%
1200 1 0.1%
1223 1 0.1%
Value Count Frequency (%) 19999 1 0.1%
19973 1 0.1%
19943 1 0.1%
19926 1 0.1%
19859 1 0.1%
19847 1 0.1%
19845 1 0.1%
19833 1 0.1%
19740 1 0.1%
19717 1 0.1%
Distinct 1427 Distinct (%) 97.1% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 14313.1034
Minimum 2094 Maximum 26999 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:51.264504 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 2094 5-th percentile 3384.55 Q1 8047 median 14235.5 Q3 20461.5 95-th percentile 25431.9 Maximum 26999 Range 24905 Interquartile range (IQR) 12414.5
Descriptive statistics
Standard deviation 7117.786044 Coefficient of variation (CV) 0.4972915967 Kurtosis -1.2149561 Mean 14313.1034 Median Absolute Deviation (MAD) 6206.5 Skewness 0.01857780789 Sum 21040262 Variance 50662878.17 Monotonicity Not monotonic
2021-08-29T08:10:51.541522 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=50)
Value Count Frequency (%) 4223 3
0.2% 9150 3
0.2% 9558 2
0.1% 12858 2
0.1% 22074 2
0.1% 25326 2
0.1% 9096 2
0.1% 13008 2
0.1% 12355 2
0.1% 7744 2
0.1% Other values (1417) 1448 98.5%
Value Count Frequency (%) 2094 1 0.1%
2097 1 0.1%
2104 1 0.1%
2112 1 0.1%
2122 1 0.1%
2125 2 0.1%
2137 1 0.1%
2227 1 0.1%
2243 1 0.1%
2253 1 0.1%
Value Count Frequency (%) 26999 1 0.1%
26997 1 0.1%
26968 1 0.1%
26959 1 0.1%
26956 1 0.1%
26933 1 0.1%
26914 1 0.1%
26897 1 0.1%
26894 1 0.1%
26862 1 0.1%
Distinct 10 Distinct (%) 0.7% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.693197279
Minimum 0 Maximum 9 Zeros 197 Zeros (%) 13.4% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:51.753525 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 0 Q1 1 median 2 Q3 4 95-th percentile 8 Maximum 9 Range 9 Interquartile range (IQR) 3
Descriptive statistics
Standard deviation 2.498009006 Coefficient of variation (CV) 0.9275254455 Kurtosis 0.01021381669 Mean 2.693197279 Median Absolute Deviation (MAD) 1 Skewness 1.026471112 Sum 3959 Variance 6.240048994 Monotonicity Not monotonic
2021-08-29T08:10:51.910075 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=10)
Value Count Frequency (%) 1 521 35.4%
0 197
13.4% 3 159
10.8% 2 146
9.9% 4 139
9.5% 7 74
5.0% 6 70
4.8% 5 63
4.3% 9 52
3.5% 8 49
3.3%
Value Count Frequency (%) 0 197
13.4% 1 521 35.4%
2 146
9.9% 3 159
10.8% 4 139
9.5% 5 63
4.3% 6 70
4.8% 7 74
5.0% 8 49
3.3% 9 52
3.5%
Value Count Frequency (%) 9 52
3.5% 8 49
3.3% 7 74
5.0% 6 70
4.8% 5 63
4.3% 4 139
9.5% 3 159
10.8% 2 146
9.9% 1 521 35.4%
0 197
13.4%
Distinct 2 Distinct (%) 0.1% Missing 0 Missing (%) 0.0% Memory size 85.2 KiB
Toggle details
Characters and Unicode
Total characters 3356 Distinct characters 5 Distinct categories 2 ? Distinct scripts 1 ? Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Sample
1st row Yes 2nd row No 3rd row Yes 4th row Yes 5th row No
Common Values Value Count Frequency (%) No 1054 71.7%
Yes 416
28.3%
Value Count Frequency (%) no 1054 71.7%
yes 416
28.3%
Most occurring characters Value Count Frequency (%) N 1054 31.4%
o 1054 31.4%
Y 416
12.4% e 416
12.4% s 416
12.4%
Most occurring categories Value Count Frequency (%) Lowercase Letter 1886 56.2%
Uppercase Letter 1470 43.8%
Most frequent character per category Lowercase Letter Value Count Frequency (%) o 1054 55.9%
e 416
22.1% s 416
22.1%
Uppercase Letter Value Count Frequency (%) N 1054 71.7%
Y 416
28.3%
Most occurring scripts Value Count Frequency (%) Latin 3356 100.0%
Most frequent character per script Latin Value Count Frequency (%) N 1054 31.4%
o 1054 31.4%
Y 416
12.4% e 416
12.4% s 416
12.4%
Most occurring blocks Value Count Frequency (%) ASCII 3356 100.0%
Most frequent character per block ASCII Value Count Frequency (%) N 1054 31.4%
o 1054 31.4%
Y 416
12.4% e 416
12.4% s 416
12.4%
Distinct 15 Distinct (%) 1.0% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 15.20952381
Minimum 11 Maximum 25 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:52.248130 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 11 5-th percentile 11 Q1 12 median 14 Q3 18 95-th percentile 22 Maximum 25 Range 14 Interquartile range (IQR) 6
Descriptive statistics
Standard deviation 3.659937717 Coefficient of variation (CV) 0.2406346025 Kurtosis -0.3005982221 Mean 15.20952381 Median Absolute Deviation (MAD) 2 Skewness 0.8211279756 Sum 22358 Variance 13.39514409 Monotonicity Not monotonic
2021-08-29T08:10:52.418100 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=15)
Value Count Frequency (%) 11 210 14.3%
13 209 14.2%
14 201 13.7%
12 198 13.5%
15 101 6.9%
18 89 6.1%
17 82
5.6% 16 78
5.3% 19 76
5.2% 22 56
3.8% Other values (5) 170 11.6%
Value Count Frequency (%) 11 210 14.3%
12 198 13.5%
13 209 14.2%
14 201 13.7%
15 101 6.9%
16 78
5.3% 17 82
5.6% 18 89 6.1%
19 76
5.2% 20 55
3.7%
Value Count Frequency (%) 25 18
1.2% 24 21
1.4% 23 28
1.9% 22 56 3.8%
21 48 3.3%
20 55 3.7%
19 76 5.2%
18 89 6.1%
17 82 5.6%
16 78 5.3%
Distinct 2 Distinct (%) 0.1% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 3.153741497
Minimum 3 Maximum 4 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:52.599839 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 3 5-th percentile 3 Q1 3 median 3 Q3 3 95-th percentile 4 Maximum 4 Range 1 Interquartile range (IQR) 0
Descriptive statistics
Standard deviation 0.3608235246 Coefficient of variation (CV) 0.1144112556 Kurtosis 1.69593867 Mean 3.153741497 Median Absolute Deviation (MAD) 0 Skewness 1.921882702 Sum 4636 Variance 0.1301936159 Monotonicity Not monotonic
2021-08-29T08:10:52.762441 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=2)
Value Count Frequency (%) 3 1244 84.6%
4 226
15.4%
Value Count Frequency (%) 3 1244 84.6%
4 226
15.4%
Value Count Frequency (%) 4 226
15.4% 3 1244 84.6%
Distinct 4 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.712244898
Minimum 1 Maximum 4 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:52.912623 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 3 Q3 4 95-th percentile 4 Maximum 4 Range 3 Interquartile range (IQR) 2
Descriptive statistics
Standard deviation 1.081208886 Coefficient of variation (CV) 0.3986398453 Kurtosis -1.184813982 Mean 2.712244898 Median Absolute Deviation (MAD) 1 Skewness -0.3028275652 Sum 3987 Variance 1.169012656 Monotonicity Not monotonic
2021-08-29T08:10:53.074621 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=4)
Value Count Frequency (%) 3 459 31.2%
4 432 29.4%
2 303 20.6%
1 276 18.8%
Value Count Frequency (%) 1 276 18.8%
2 303 20.6%
3 459 31.2%
4 432 29.4%
Value Count Frequency (%) 4 432 29.4%
3 459 31.2%
2 303 20.6%
1 276 18.8%
Distinct 1 Distinct (%) 0.1% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 80
Minimum 80 Maximum 80 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:53.246593 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 80 5-th percentile 80 Q1 80 median 80 Q3 80 95-th percentile 80 Maximum 80 Range 0 Interquartile range (IQR) 0
Descriptive statistics
Standard deviation 0 Coefficient of variation (CV) 0 Kurtosis 0 Mean 80 Median Absolute Deviation (MAD) 0 Skewness 0 Sum 117600 Variance 0 Monotonicity Increasing
2021-08-29T08:10:53.435590 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=1)
Value Count Frequency (%) 80 1470 100.0%
Value Count Frequency (%) 80 1470 100.0%
Value Count Frequency (%) 80 1470 100.0%
Distinct 4 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 0.793877551
Minimum 0 Maximum 3 Zeros 631 Zeros (%) 42.9% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:53.585593 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 0 Q1 0 median 1 Q3 1 95-th percentile 3 Maximum 3 Range 3 Interquartile range (IQR) 1
Descriptive statistics
Standard deviation 0.8520766679 Coefficient of variation (CV) 1.073309942 Kurtosis 0.3646343338 Mean 0.793877551 Median Absolute Deviation (MAD) 1 Skewness 0.9689803168 Sum 1167 Variance 0.726034648 Monotonicity Not monotonic
2021-08-29T08:10:53.738689 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=4)
Value Count Frequency (%) 0 631 42.9%
1 596 40.5%
2 158
10.7% 3 85
5.8%
Value Count Frequency (%) 0 631 42.9%
1 596 40.5%
2 158
10.7% 3 85
5.8%
Value Count Frequency (%) 3 85
5.8% 2 158
10.7% 1 596 40.5%
0 631 42.9%
Distinct 40 Distinct (%) 2.7% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 11.27959184
Minimum 0 Maximum 40 Zeros 11 Zeros (%) 0.7% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:53.946271 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 1 Q1 6 median 10 Q3 15 95-th percentile 28 Maximum 40 Range 40 Interquartile range (IQR) 9
Descriptive statistics
Standard deviation 7.780781676 Coefficient of variation (CV) 0.6898105701 Kurtosis 0.9182695366 Mean 11.27959184 Median Absolute Deviation (MAD) 4 Skewness 1.117171853 Sum 16581 Variance 60.54056348 Monotonicity Not monotonic
2021-08-29T08:10:54.160631 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=40)
Value Count Frequency (%) 10 202
13.7% 6 125
8.5% 8 103
7.0% 9 96
6.5% 5 88
6.0% 7 81
5.5% 1 81
5.5% 4 63
4.3% 12 48
3.3% 3 42
2.9% Other values (30) 541 36.8%
Value Count Frequency (%) 0 11
0.7% 1 81 5.5%
2 31
2.1% 3 42
2.9% 4 63 4.3%
5 88 6.0%
6 125 8.5%
7 81 5.5%
8 103 7.0%
9 96 6.5%
Value Count Frequency (%) 40 2
0.1% 38 1
0.1% 37 4 0.3%
36 6 0.4%
35 3
0.2% 34 5 0.3%
33 7 0.5%
32 9 0.6%
31 9 0.6%
30 7 0.5%
Distinct 7 Distinct (%) 0.5% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.799319728
Minimum 0 Maximum 6 Zeros 54 Zeros (%) 3.7% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:54.339492 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 1 Q1 2 median 3 Q3 3 95-th percentile 5 Maximum 6 Range 6 Interquartile range (IQR) 1
Descriptive statistics
Standard deviation 1.289270621 Coefficient of variation (CV) 0.4605656896 Kurtosis 0.494992986 Mean 2.799319728 Median Absolute Deviation (MAD) 1 Skewness 0.5531241711 Sum 4115 Variance 1.662218734 Monotonicity Not monotonic
2021-08-29T08:10:54.484818 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=7)
Value Count Frequency (%) 2 547 37.2%
3 491 33.4%
4 123
8.4% 5 119
8.1% 1 71
4.8% 6 65
4.4% 0 54
3.7%
Value Count Frequency (%) 0 54
3.7% 1 71
4.8% 2 547 37.2%
3 491 33.4%
4 123
8.4% 5 119
8.1% 6 65
4.4%
Value Count Frequency (%) 6 65
4.4% 5 119
8.1% 4 123
8.4% 3 491 33.4%
2 547 37.2%
1 71
4.8% 0 54
3.7%
Distinct 4 Distinct (%) 0.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.76122449
Minimum 1 Maximum 4 Zeros 0 Zeros (%) 0.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:54.645540 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 1 5-th percentile 1 Q1 2 median 3 Q3 3 95-th percentile 4 Maximum 4 Range 3 Interquartile range (IQR) 1
Descriptive statistics
Standard deviation 0.7064758297 Coefficient of variation (CV) 0.2558559915 Kurtosis 0.4194604953 Mean 2.76122449 Median Absolute Deviation (MAD) 0 Skewness -0.5524802991 Sum 4059 Variance 0.499108098 Monotonicity Not monotonic
2021-08-29T08:10:54.817055 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=4)
Value Count Frequency (%) 3 893 60.7%
2 344
23.4% 4 153
10.4% 1 80
5.4%
Value Count Frequency (%) 1 80
5.4% 2 344
23.4% 3 893 60.7%
4 153
10.4%
Value Count Frequency (%) 4 153
10.4% 3 893 60.7%
2 344
23.4% 1 80
5.4%
Distinct 37 Distinct (%) 2.5% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 7.008163265
Minimum 0 Maximum 40 Zeros 44 Zeros (%) 3.0% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:55.035058 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 1 Q1 3 median 5 Q3 9 95-th percentile 20 Maximum 40 Range 40 Interquartile range (IQR) 6
Descriptive statistics
Standard deviation 6.126525152 Coefficient of variation (CV) 0.8741984056 Kurtosis 3.935508756 Mean 7.008163265 Median Absolute Deviation (MAD) 3 Skewness 1.764529454 Sum 10302 Variance 37.53431044 Monotonicity Not monotonic
2021-08-29T08:10:55.291054 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=37)
Value Count Frequency (%) 5 196 13.3%
1 171 11.6%
3 128 8.7%
2 127 8.6%
10 120 8.2%
4 110
7.5% 7 90
6.1% 9 82
5.6% 8 80
5.4% 6 76
5.2% Other values (27) 290 19.7%
Value Count Frequency (%) 0 44
3.0% 1 171 11.6%
2 127 8.6%
3 128 8.7%
4 110 7.5%
5 196 13.3%
6 76
5.2% 7 90 6.1%
8 80 5.4%
9 82 5.6%
Value Count Frequency (%) 40 1
0.1% 37 1
0.1% 36 2
0.1% 34 1
0.1% 33 5 0.3%
32 3 0.2%
31 3 0.2%
30 1
0.1% 29 2
0.1% 27 2
0.1%
Distinct 19 Distinct (%) 1.3% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 4.229251701
Minimum 0 Maximum 18 Zeros 244 Zeros (%) 16.6% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:55.492924 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 0 Q1 2 median 3 Q3 7 95-th percentile 11 Maximum 18 Range 18 Interquartile range (IQR) 5
Descriptive statistics
Standard deviation 3.623137035 Coefficient of variation (CV) 0.856685128 Kurtosis 0.4774207735 Mean 4.229251701 Median Absolute Deviation (MAD) 3 Skewness 0.9173631563 Sum 6217 Variance 13.12712197 Monotonicity Not monotonic
2021-08-29T08:10:55.659925 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=19)
Value Count Frequency (%) 2 372 25.3%
0 244 16.6%
7 222 15.1%
3 135
9.2% 4 104
7.1% 8 89
6.1% 9 67
4.6% 1 57
3.9% 6 37
2.5% 5 36
2.4% Other values (9) 107
7.3%
Value Count Frequency (%) 0 244 16.6%
1 57
3.9% 2 372 25.3%
3 135
9.2% 4 104
7.1% 5 36
2.4% 6 37
2.5% 7 222 15.1%
8 89
6.1% 9 67
4.6%
Value Count Frequency (%) 18 2
0.1% 17 4
0.3% 16 7
0.5% 15 8
0.5% 14 11
0.7% 13 14
1.0% 12 10
0.7% 11 22
1.5% 10 29 2.0%
9 67 4.6%
Distinct 16 Distinct (%) 1.1% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 2.187755102
Minimum 0 Maximum 15 Zeros 581 Zeros (%) 39.5% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:55.861405 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 0 Q1 0 median 1 Q3 3 95-th percentile 9 Maximum 15 Range 15 Interquartile range (IQR) 3
Descriptive statistics
Standard deviation 3.222430279 Coefficient of variation (CV) 1.472939213 Kurtosis 3.612673115 Mean 2.187755102 Median Absolute Deviation (MAD) 1 Skewness 1.984289983 Sum 3216 Variance 10.3840569 Monotonicity Not monotonic
2021-08-29T08:10:56.044938 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=16)
Value Count Frequency (%) 0 581 39.5%
1 357 24.3%
2 159
10.8% 7 76
5.2% 4 61
4.1% 3 52
3.5% 5 45
3.1% 6 32
2.2% 11 24
1.6% 8 18
1.2% Other values (6) 65
4.4%
Value Count Frequency (%) 0 581 39.5%
1 357 24.3%
2 159
10.8% 3 52
3.5% 4 61
4.1% 5 45
3.1% 6 32
2.2% 7 76
5.2% 8 18
1.2% 9 17
1.2%
Value Count Frequency (%) 15 13
0.9% 14 9
0.6% 13 10
0.7% 12 10
0.7% 11 24
1.6% 10 6
0.4% 9 17
1.2% 8 18
1.2% 7 76 5.2%
6 32 2.2%
Distinct 18 Distinct (%) 1.2% Missing 0 Missing (%) 0.0% Infinite 0 Infinite (%) 0.0% Mean 4.123129252
Minimum 0 Maximum 17 Zeros 263 Zeros (%) 17.9% Negative 0 Negative (%) 0.0% Memory size 11.6 KiB
2021-08-29T08:10:56.223067 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Toggle details
Quantile statistics
Minimum 0 5-th percentile 0 Q1 2 median 3 Q3 7 95-th percentile 10 Maximum 17 Range 17 Interquartile range (IQR) 5
Descriptive statistics
Standard deviation 3.568136121 Coefficient of variation (CV) 0.8653951654 Kurtosis 0.1710580839 Mean 4.123129252 Median Absolute Deviation (MAD) 3 Skewness 0.833450992 Sum 6061 Variance 12.73159537 Monotonicity Not monotonic
2021-08-29T08:10:56.392066 image/svg+xml Matplotlib v3.4.2, https://matplotlib.org/ Histogram with fixed size bins (bins=18)
Value Count Frequency (%) 2 344 23.4%
0 263 17.9%
7 216 14.7%
3 142 9.7%
8 107
7.3% 4 98
6.7% 1 76
5.2% 9 64
4.4% 5 31
2.1% 6 29
2.0% Other values (8) 100
6.8%
Value Count Frequency (%) 0 263 17.9%
1 76
5.2% 2 344 23.4%
3 142 9.7%
4 98
6.7% 5 31
2.1% 6 29
2.0% 7 216 14.7%
8 107
7.3% 9 64
4.4%
Value Count Frequency (%) 17 7
0.5% 16 2
0.1% 15 5
0.3% 14 5
0.3% 13 14
1.0% 12 18
1.2% 11 22
1.5% 10 27
1.8% 9 64 4.4%
8 107 7.3%
\ No newline at end of file
diff --git a/007/solution/catboost_info/catboost_training.json b/007/solution/catboost_info/catboost_training.json
new file mode 100644
index 00000000..9457164f
--- /dev/null
+++ b/007/solution/catboost_info/catboost_training.json
@@ -0,0 +1,804 @@
+{
+"meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":800,"learn_sets":["learn"],"name":"experiment"},
+"iterations":[
+{"learn":[0.5886758439],"iteration":0,"passed_time":0.007136191218,"remaining_time":5.701816783},
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diff --git a/007/solution/catboost_info/time_left.tsv b/007/solution/catboost_info/time_left.tsv
new file mode 100644
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diff --git a/007/solution/readme.md b/007/solution/readme.md
index 7862e6b0..616d022f 100644
--- a/007/solution/readme.md
+++ b/007/solution/readme.md
@@ -1,15 +1,38 @@
## Solutions
-### Inital Format:
+### Model Name and File Name: (IBM_HR.ipynb)
+1 svm.SVC
+2 SGDClassifier
+3 Perceptron
+4 MultinomialNB
+5 PassiveAggressiveClassifier
+6 GaussianNB
+7 GaussianProcessClassifier
+8 KNeighborsClassifier
+9 RandomForestClassifier
+10 AdaBoostClassifier
+11 ExtraTreesClassifier
+12 GradientBoostingClassifier
+13 MLPClassifier
+14 QuadraticDiscriminantAnalysis
+15 xgboost.XGBClassifier
+16 cb.CatBoostClassifier
+17 CNN
-Model Name and File Name: [E.g. Random Forest Classifier, RFR_clf.nb]
+### Description:
+Sturge’s rule:
+Number of Bins = 1+log2(N) (Number of Samples)
+In this case 10 bins means 10 folds
-Description: [E.g. a random forest classifier from scikit-learn]
+svm.SCV 85% and remaining algorithm above 90%
-Further details:
-[E.g. I did a 3-fold cv-grid search to find the best number of depth hyperparameter of RFR_clf]
-
-Model Accuracy:
-- Confusion matrix: [...]
-- F1 score: [...]
+Grid Random Search from large values to closer to Best Parameters.
+### Further details:
+Fold:KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit
+### Model Accuracy (Classification Report):
+- Confusion matrix
+- F1 score
+- precision
+- recall
+- support
\ No newline at end of file
diff --git a/007/solution/sweetviz_report.html b/007/solution/sweetviz_report.html
new file mode 100644
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+
+
+
+
+
DISTINCT:
+
+ 16
+
+
+ (1%)
+
+
+
+
+
+
ZEROES:
+
+ 581
+
+
+ (40%)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 35
+
+
+
YearsWithCurrManager
+
+
+
+
VALUES:
+
+ 1,470
+
+
+ (100%)
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
DISTINCT:
+
+ 18
+
+
+ (1%)
+
+
+
+
+
+
ZEROES:
+
+ 263
+
+
+ (18%)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Associations
+
+ [Only including dataset "DataFrame"]
+ ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical ,
+ (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP).
+ • Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.
+
+
+
+
+
+
+
+
+
Associations
+
+ [Only including dataset "None"]
+ ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical ,
+ (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP).
+ • Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
TotalWorkingYears
+
0.68
+
+
+
+
YearsAtCompany
+
0.31
+
+
+
YearsSinceLastPromotion
+
0.22
+
+
+
YearsInCurrentRole
+
0.21
+
+
+
YearsWithCurrManager
+
0.20
+
+
+
+
+
+
EmployeeNumber
+
-0.01
+
+
+
PercentSalaryHike
+
0.00
+
+
+
DistanceFromHome
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
NumCompaniesWorked
+
0.44
+
+
+
+
+
+
+
StockOptionLevel
+
0.11
+
+
+
TrainingTimesLastYear
+
0.09
+
+
+
EducationField
+
0.06
+
+
+
RelationshipSatisfaction
+
0.06
+
+
+
JobInvolvement
+
0.04
+
+
+
+
+
EnvironmentSatisfaction
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
Attrition
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
StockOptionLevel
+
0.02
+
+
+
+
+
+
BusinessTravel
+
0.01
+
+
+
JobInvolvement
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON Attrition:
+
+
+
+
+
+
StockOptionLevel
+
0.05
+
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
JobInvolvement
+
0.02
+
+
+
BusinessTravel
+
0.02
+
+
+
EnvironmentSatisfaction
+
0.02
+
+
+
JobSatisfaction
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
Attrition
+ CORRELATION RATIO WITH...
+
+
+
TotalWorkingYears
+
0.17
+
+
+
YearsInCurrentRole
+
0.16
+
+
+
+
+
YearsWithCurrManager
+
0.16
+
+
+
YearsAtCompany
+
0.13
+
+
+
DistanceFromHome
+
0.08
+
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
+
PercentSalaryHike
+
0.01
+
+
+
EmployeeNumber
+
0.01
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Travel_Rarely
+
+
+
+
+
+
+
+
Travel_Frequently
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
BusinessTravel
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON BusinessTravel:
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
BusinessTravel
+ CORRELATION RATIO WITH...
+
+
+
+
PercentSalaryHike
+
0.04
+
+
+
TotalWorkingYears
+
0.03
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsWithCurrManager
+
0.02
+
+
+
EmployeeNumber
+
0.02
+
+
+
YearsAtCompany
+
0.02
+
+
+
+
+
YearsInCurrentRole
+
0.01
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
EmployeeNumber
+
-0.05
+
+
+
YearsAtCompany
+
-0.03
+
+
+
YearsSinceLastPromotion
+
-0.03
+
+
+
+
YearsWithCurrManager
+
-0.03
+
+
+
+
PercentSalaryHike
+
0.02
+
+
+
TotalWorkingYears
+
0.01
+
+
+
+
YearsInCurrentRole
+
0.01
+
+
+
+
DistanceFromHome
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
NumCompaniesWorked
+
0.09
+
+
+
EducationField
+
0.08
+
+
+
+
JobSatisfaction
+
0.06
+
+
+
+
StockOptionLevel
+
0.05
+
+
+
JobInvolvement
+
0.05
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
+
+
WorkLifeBalance
+
0.04
+
+
+
+
RelationshipSatisfaction
+
0.03
+
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Research & Development
+
+
+
+
+
+
+
+
+
Human Resources
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
Department
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
EducationField
+
0.15
+
+
+
+
+
WorkLifeBalance
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON Department:
+
+
+
+
EducationField
+
0.27
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
Department
+ CORRELATION RATIO WITH...
+
+
+
EmployeeNumber
+
0.07
+
+
+
+
YearsInCurrentRole
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.04
+
+
+
YearsWithCurrManager
+
0.04
+
+
+
PercentSalaryHike
+
0.04
+
+
+
+
YearsAtCompany
+
0.03
+
+
+
+
+
+
DistanceFromHome
+
0.02
+
+
+
TotalWorkingYears
+
0.02
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
PercentSalaryHike
+
0.04
+
+
+
EmployeeNumber
+
0.03
+
+
+
+
+
YearsInCurrentRole
+
0.02
+
+
+
MonthlyIncome
+
-0.02
+
+
+
YearsWithCurrManager
+
0.01
+
+
+
YearsSinceLastPromotion
+
0.01
+
+
+
YearsAtCompany
+
0.01
+
+
+
+
TotalWorkingYears
+
0.00
+
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
NumCompaniesWorked
+
0.10
+
+
+
+
StockOptionLevel
+
0.09
+
+
+
+
+
TrainingTimesLastYear
+
0.06
+
+
+
EducationField
+
0.05
+
+
+
RelationshipSatisfaction
+
0.04
+
+
+
WorkLifeBalance
+
0.04
+
+
+
+
JobInvolvement
+
0.03
+
+
+
+
EnvironmentSatisfaction
+
0.03
+
+
+
PerformanceRating
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
Education
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
+
EducationField
+
0.01
+
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON Education:
+
+
+
NumCompaniesWorked
+
0.03
+
+
+
+
+
EducationField
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
StockOptionLevel
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
Education
+ CORRELATION RATIO WITH...
+
+
+
+
TotalWorkingYears
+
0.15
+
+
+
+
YearsAtCompany
+
0.08
+
+
+
YearsWithCurrManager
+
0.07
+
+
+
YearsInCurrentRole
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.06
+
+
+
EmployeeNumber
+
0.05
+
+
+
+
+
PercentSalaryHike
+
0.04
+
+
+
DistanceFromHome
+
0.04
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Life Sciences
+
+
+
+
+
+
+
+
+
+
Technical Degree
+
+
+
+
+
+
+
+
+
Human Resources
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
EducationField
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
StockOptionLevel
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
RelationshipSatisfaction
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON EducationField:
+
+
+
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
RelationshipSatisfaction
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
EducationField
+ CORRELATION RATIO WITH...
+
+
+
+
+
+
+
PercentSalaryHike
+
0.06
+
+
+
TotalWorkingYears
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.05
+
+
+
YearsAtCompany
+
0.05
+
+
+
YearsInCurrentRole
+
0.05
+
+
+
DistanceFromHome
+
0.05
+
+
+
YearsWithCurrManager
+
0.05
+
+
+
+
EmployeeNumber
+
0.04
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
EmployeeCount
+ PROVIDES INFORMATION ON...
+
+
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON EmployeeCount:
+
+
+
+
BusinessTravel
+
1.00
+
+
+
+
+
EducationField
+
1.00
+
+
+
EnvironmentSatisfaction
+
1.00
+
+
+
+
JobInvolvement
+
1.00
+
+
+
+
+
JobSatisfaction
+
1.00
+
+
+
+
NumCompaniesWorked
+
1.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
EmployeeCount
+ CORRELATION RATIO WITH...
+
+
+
+
+
DistanceFromHome
+
0.00
+
+
+
EmployeeNumber
+
0.00
+
+
+
+
+
+
PercentSalaryHike
+
0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
YearsAtCompany
+
0.00
+
+
+
YearsInCurrentRole
+
0.00
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
YearsWithCurrManager
+
0.00
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
MonthlyIncome
+
-0.01
+
+
+
TotalWorkingYears
+
-0.01
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
+
YearsAtCompany
+
-0.01
+
+
+
+
YearsWithCurrManager
+
-0.01
+
+
+
YearsSinceLastPromotion
+
-0.01
+
+
+
YearsInCurrentRole
+
-0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
NumCompaniesWorked
+
0.09
+
+
+
RelationshipSatisfaction
+
0.08
+
+
+
TrainingTimesLastYear
+
0.07
+
+
+
+
StockOptionLevel
+
0.06
+
+
+
+
+
EnvironmentSatisfaction
+
0.05
+
+
+
JobSatisfaction
+
0.05
+
+
+
+
EducationField
+
0.04
+
+
+
JobInvolvement
+
0.03
+
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
EnvironmentSatisfaction
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
EnvironmentSatisfaction
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
EducationField
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON EnvironmentSatisfaction:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
+
EducationField
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
EnvironmentSatisfaction
+ CORRELATION RATIO WITH...
+
+
+
+
EmployeeNumber
+
0.05
+
+
+
+
PercentSalaryHike
+
0.04
+
+
+
YearsInCurrentRole
+
0.04
+
+
+
TotalWorkingYears
+
0.04
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsAtCompany
+
0.02
+
+
+
+
YearsWithCurrManager
+
0.01
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
Gender
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
BusinessTravel
+
0.00
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
EducationField
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON Gender:
+
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
+
+
+
EducationField
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
Gender
+ CORRELATION RATIO WITH...
+
+
+
TotalWorkingYears
+
0.05
+
+
+
YearsInCurrentRole
+
0.04
+
+
+
+
+
+
YearsWithCurrManager
+
0.03
+
+
+
YearsAtCompany
+
0.03
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
EmployeeNumber
+
0.02
+
+
+
+
PercentSalaryHike
+
0.00
+
+
+
DistanceFromHome
+
0.00
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
EmployeeNumber
+
0.04
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsSinceLastPromotion
+
-0.03
+
+
+
+
YearsInCurrentRole
+
-0.02
+
+
+
+
YearsWithCurrManager
+
-0.02
+
+
+
YearsAtCompany
+
-0.02
+
+
+
MonthlyIncome
+
-0.02
+
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
TotalWorkingYears
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
JobSatisfaction
+
0.08
+
+
+
StockOptionLevel
+
0.07
+
+
+
NumCompaniesWorked
+
0.07
+
+
+
EducationField
+
0.06
+
+
+
EnvironmentSatisfaction
+
0.06
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
+
JobInvolvement
+
0.05
+
+
+
+
+
WorkLifeBalance
+
0.04
+
+
+
+
BusinessTravel
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
JobInvolvement
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
+
EducationField
+
0.00
+
+
+
+
BusinessTravel
+
0.00
+
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON JobInvolvement:
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
+
+
EducationField
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
JobInvolvement
+ CORRELATION RATIO WITH...
+
+
+
+
+
PercentSalaryHike
+
0.05
+
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
EmployeeNumber
+
0.03
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
YearsWithCurrManager
+
0.03
+
+
+
YearsAtCompany
+
0.03
+
+
+
+
YearsInCurrentRole
+
0.01
+
+
+
TotalWorkingYears
+
0.01
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
JobLevel
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
EducationField
+
0.02
+
+
+
+
StockOptionLevel
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON JobLevel:
+
+
+
+
+
NumCompaniesWorked
+
0.03
+
+
+
EducationField
+
0.02
+
+
+
+
+
StockOptionLevel
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
JobInvolvement
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
JobLevel
+ CORRELATION RATIO WITH...
+
+
+
+
TotalWorkingYears
+
0.80
+
+
+
YearsAtCompany
+
0.54
+
+
+
+
YearsInCurrentRole
+
0.41
+
+
+
YearsWithCurrManager
+
0.39
+
+
+
YearsSinceLastPromotion
+
0.36
+
+
+
DistanceFromHome
+
0.10
+
+
+
+
EmployeeNumber
+
0.06
+
+
+
PercentSalaryHike
+
0.05
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sales Executive
+
+
+
+
+
+
+
+
Research Scientist
+
+
+
+
+
+
+
+
Laboratory Technician
+
+
+
+
+
+
+
+
Manufacturing Director
+
+
+
+
+
+
+
+
Healthcare Representative
+
+
+
+
+
+
+
+
+
Sales Representative
+
+
+
+
+
+
+
+
Research Director
+
+
+
+
+
+
+
+
Human Resources
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
JobRole
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
EducationField
+
0.14
+
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
+
StockOptionLevel
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON JobRole:
+
+
+
+
+
EducationField
+
0.10
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
JobRole
+ CORRELATION RATIO WITH...
+
+
+
+
TotalWorkingYears
+
0.66
+
+
+
YearsAtCompany
+
0.44
+
+
+
+
YearsInCurrentRole
+
0.33
+
+
+
YearsWithCurrManager
+
0.32
+
+
+
YearsSinceLastPromotion
+
0.30
+
+
+
EmployeeNumber
+
0.09
+
+
+
PercentSalaryHike
+
0.08
+
+
+
DistanceFromHome
+
0.07
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
JobSatisfaction
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
PerformanceRating
+
0.00
+
+
+
+
+
JobInvolvement
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON JobSatisfaction:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
+
EducationField
+
0.00
+
+
+
+
+
JobInvolvement
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
PerformanceRating
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
JobSatisfaction
+ CORRELATION RATIO WITH...
+
+
+
+
+
EmployeeNumber
+
0.05
+
+
+
PercentSalaryHike
+
0.05
+
+
+
YearsWithCurrManager
+
0.04
+
+
+
+
TotalWorkingYears
+
0.02
+
+
+
YearsSinceLastPromotion
+
0.02
+
+
+
DistanceFromHome
+
0.02
+
+
+
YearsInCurrentRole
+
0.02
+
+
+
+
YearsAtCompany
+
0.01
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
MaritalStatus
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
StockOptionLevel
+
0.37
+
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
BusinessTravel
+
0.00
+
+
+
+
EducationField
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON MaritalStatus:
+
+
+
StockOptionLevel
+
0.40
+
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
EducationField
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
+
WorkLifeBalance
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
MaritalStatus
+ CORRELATION RATIO WITH...
+
+
+
+
TotalWorkingYears
+
0.09
+
+
+
+
YearsInCurrentRole
+
0.09
+
+
+
+
YearsAtCompany
+
0.07
+
+
+
YearsSinceLastPromotion
+
0.06
+
+
+
EmployeeNumber
+
0.05
+
+
+
YearsWithCurrManager
+
0.05
+
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
PercentSalaryHike
+
0.03
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
TotalWorkingYears
+
0.77
+
+
+
YearsAtCompany
+
0.51
+
+
+
+
YearsInCurrentRole
+
0.36
+
+
+
YearsSinceLastPromotion
+
0.34
+
+
+
YearsWithCurrManager
+
0.34
+
+
+
+
PercentSalaryHike
+
-0.03
+
+
+
DistanceFromHome
+
-0.02
+
+
+
+
EmployeeNumber
+
-0.01
+
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
+
NumCompaniesWorked
+
0.24
+
+
+
+
+
StockOptionLevel
+
0.09
+
+
+
+
EducationField
+
0.08
+
+
+
+
BusinessTravel
+
0.04
+
+
+
TrainingTimesLastYear
+
0.04
+
+
+
WorkLifeBalance
+
0.04
+
+
+
RelationshipSatisfaction
+
0.03
+
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
YearsWithCurrManager
+
-0.04
+
+
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
TotalWorkingYears
+
0.03
+
+
+
YearsAtCompany
+
-0.02
+
+
+
+
YearsInCurrentRole
+
-0.01
+
+
+
EmployeeNumber
+
0.01
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
NumCompaniesWorked
+
0.08
+
+
+
+
TrainingTimesLastYear
+
0.06
+
+
+
+
EnvironmentSatisfaction
+
0.05
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
+
StockOptionLevel
+
0.05
+
+
+
EducationField
+
0.04
+
+
+
+
+
JobInvolvement
+
0.04
+
+
+
JobSatisfaction
+
0.03
+
+
+
WorkLifeBalance
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
NumCompaniesWorked
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
+
+
EducationField
+
0.02
+
+
+
WorkLifeBalance
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
+
+
JobInvolvement
+
0.01
+
+
+
PerformanceRating
+
0.01
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON NumCompaniesWorked:
+
+
+
+
+
+
EducationField
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
NumCompaniesWorked
+ CORRELATION RATIO WITH...
+
+
+
+
TotalWorkingYears
+
0.37
+
+
+
+
YearsAtCompany
+
0.18
+
+
+
YearsWithCurrManager
+
0.16
+
+
+
YearsInCurrentRole
+
0.16
+
+
+
DistanceFromHome
+
0.10
+
+
+
+
YearsSinceLastPromotion
+
0.09
+
+
+
EmployeeNumber
+
0.09
+
+
+
+
PercentSalaryHike
+
0.08
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
Over18
+ PROVIDES INFORMATION ON...
+
+
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON Over18:
+
+
+
+
BusinessTravel
+
1.00
+
+
+
+
+
EducationField
+
1.00
+
+
+
+
EnvironmentSatisfaction
+
1.00
+
+
+
+
JobInvolvement
+
1.00
+
+
+
+
+
JobSatisfaction
+
1.00
+
+
+
+
NumCompaniesWorked
+
1.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
Over18
+ CORRELATION RATIO WITH...
+
+
+
+
+
DistanceFromHome
+
0.00
+
+
+
EmployeeNumber
+
0.00
+
+
+
+
+
+
PercentSalaryHike
+
0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
YearsAtCompany
+
0.00
+
+
+
YearsInCurrentRole
+
0.00
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
YearsWithCurrManager
+
0.00
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
OverTime
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
BusinessTravel
+
0.00
+
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON OverTime:
+
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
+
WorkLifeBalance
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
OverTime
+ CORRELATION RATIO WITH...
+
+
+
YearsWithCurrManager
+
0.04
+
+
+
YearsInCurrentRole
+
0.03
+
+
+
+
DistanceFromHome
+
0.03
+
+
+
EmployeeNumber
+
0.02
+
+
+
+
TotalWorkingYears
+
0.01
+
+
+
YearsSinceLastPromotion
+
0.01
+
+
+
YearsAtCompany
+
0.01
+
+
+
+
+
+
PercentSalaryHike
+
0.01
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
DistanceFromHome
+
0.04
+
+
+
YearsAtCompany
+
-0.04
+
+
+
MonthlyIncome
+
-0.03
+
+
+
+
YearsSinceLastPromotion
+
-0.02
+
+
+
TotalWorkingYears
+
-0.02
+
+
+
EmployeeNumber
+
-0.01
+
+
+
YearsWithCurrManager
+
-0.01
+
+
+
+
+
+
YearsInCurrentRole
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
PerformanceRating
+
0.77
+
+
+
NumCompaniesWorked
+
0.08
+
+
+
+
EducationField
+
0.06
+
+
+
StockOptionLevel
+
0.05
+
+
+
+
WorkLifeBalance
+
0.05
+
+
+
JobSatisfaction
+
0.05
+
+
+
JobInvolvement
+
0.05
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
EnvironmentSatisfaction
+
0.04
+
+
+
BusinessTravel
+
0.04
+
+
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
PerformanceRating
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON PerformanceRating:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
PerformanceRating
+ CORRELATION RATIO WITH...
+
+
+
PercentSalaryHike
+
0.77
+
+
+
YearsInCurrentRole
+
0.03
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsWithCurrManager
+
0.02
+
+
+
EmployeeNumber
+
0.02
+
+
+
YearsSinceLastPromotion
+
0.02
+
+
+
+
+
TotalWorkingYears
+
0.01
+
+
+
YearsAtCompany
+
0.00
+
+
+
+
+
+
+
+
+
+
+
+
+
+
RelationshipSatisfaction
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
RelationshipSatisfaction
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
EducationField
+
0.01
+
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
+
+
+
+
WorkLifeBalance
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON RelationshipSatisfaction:
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
+
+
WorkLifeBalance
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
BusinessTravel
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
RelationshipSatisfaction
+ CORRELATION RATIO WITH...
+
+
+
YearsSinceLastPromotion
+
0.09
+
+
+
EmployeeNumber
+
0.08
+
+
+
YearsWithCurrManager
+
0.06
+
+
+
+
YearsInCurrentRole
+
0.05
+
+
+
+
YearsAtCompany
+
0.05
+
+
+
+
PercentSalaryHike
+
0.05
+
+
+
DistanceFromHome
+
0.04
+
+
+
+
+
TotalWorkingYears
+
0.02
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
StandardHours
+ PROVIDES INFORMATION ON...
+
+
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON StandardHours:
+
+
+
+
BusinessTravel
+
1.00
+
+
+
+
+
EducationField
+
1.00
+
+
+
+
EnvironmentSatisfaction
+
1.00
+
+
+
+
JobInvolvement
+
1.00
+
+
+
+
+
JobSatisfaction
+
1.00
+
+
+
+
NumCompaniesWorked
+
1.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
StandardHours
+ CORRELATION RATIO WITH...
+
+
+
+
+
DistanceFromHome
+
0.00
+
+
+
EmployeeNumber
+
0.00
+
+
+
+
+
+
PercentSalaryHike
+
0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
YearsAtCompany
+
0.00
+
+
+
YearsInCurrentRole
+
0.00
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
YearsWithCurrManager
+
0.00
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
StockOptionLevel
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
+
+
+
EducationField
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
+
JobInvolvement
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON StockOptionLevel:
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
StockOptionLevel
+ CORRELATION RATIO WITH...
+
+
+
+
TotalWorkingYears
+
0.10
+
+
+
+
DistanceFromHome
+
0.09
+
+
+
YearsAtCompany
+
0.08
+
+
+
YearsInCurrentRole
+
0.08
+
+
+
+
YearsWithCurrManager
+
0.06
+
+
+
EmployeeNumber
+
0.06
+
+
+
+
PercentSalaryHike
+
0.05
+
+
+
+
YearsSinceLastPromotion
+
0.05
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
+
+
YearsAtCompany
+
0.63
+
+
+
YearsInCurrentRole
+
0.46
+
+
+
YearsWithCurrManager
+
0.46
+
+
+
YearsSinceLastPromotion
+
0.40
+
+
+
+
PercentSalaryHike
+
-0.02
+
+
+
+
EmployeeNumber
+
-0.01
+
+
+
DistanceFromHome
+
0.00
+
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
+
NumCompaniesWorked
+
0.37
+
+
+
+
+
StockOptionLevel
+
0.10
+
+
+
+
TrainingTimesLastYear
+
0.07
+
+
+
EducationField
+
0.06
+
+
+
+
EnvironmentSatisfaction
+
0.04
+
+
+
BusinessTravel
+
0.03
+
+
+
WorkLifeBalance
+
0.03
+
+
+
RelationshipSatisfaction
+
0.02
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
TrainingTimesLastYear
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
TrainingTimesLastYear
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
EducationField
+
0.01
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
+
JobInvolvement
+
0.01
+
+
+
+
+
JobSatisfaction
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON TrainingTimesLastYear:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
+
+
+
+
JobInvolvement
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
TrainingTimesLastYear
+ CORRELATION RATIO WITH...
+
+
+
+
EmployeeNumber
+
0.07
+
+
+
YearsAtCompany
+
0.07
+
+
+
TotalWorkingYears
+
0.07
+
+
+
+
DistanceFromHome
+
0.06
+
+
+
+
+
YearsWithCurrManager
+
0.05
+
+
+
YearsInCurrentRole
+
0.05
+
+
+
+
YearsSinceLastPromotion
+
0.04
+
+
+
PercentSalaryHike
+
0.03
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1)
+
+
WorkLifeBalance
+ PROVIDES INFORMATION ON...
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
+
StockOptionLevel
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
+
+
JobInvolvement
+
0.00
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
THESE FEATURES GIVE INFORMATION
+ ON WorkLifeBalance:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
+
EducationField
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
+
+
+
+
JobSatisfaction
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
+
+
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1)
+
+
WorkLifeBalance
+ CORRELATION RATIO WITH...
+
+
+
YearsInCurrentRole
+
0.06
+
+
+
PercentSalaryHike
+
0.05
+
+
+
+
DistanceFromHome
+
0.04
+
+
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
+
YearsAtCompany
+
0.03
+
+
+
TotalWorkingYears
+
0.03
+
+
+
+
EmployeeNumber
+
0.02
+
+
+
YearsWithCurrManager
+
0.02
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
YearsWithCurrManager
+
0.77
+
+
+
YearsInCurrentRole
+
0.76
+
+
+
TotalWorkingYears
+
0.63
+
+
+
YearsSinceLastPromotion
+
0.62
+
+
+
+
+
PercentSalaryHike
+
-0.04
+
+
+
+
+
+
EmployeeNumber
+
-0.01
+
+
+
DistanceFromHome
+
0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
+
NumCompaniesWorked
+
0.18
+
+
+
+
StockOptionLevel
+
0.08
+
+
+
+
+
TrainingTimesLastYear
+
0.07
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
EducationField
+
0.05
+
+
+
+
JobInvolvement
+
0.03
+
+
+
+
WorkLifeBalance
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
YearsAtCompany
+
0.76
+
+
+
YearsWithCurrManager
+
0.71
+
+
+
YearsSinceLastPromotion
+
0.55
+
+
+
TotalWorkingYears
+
0.46
+
+
+
+
+
+
DistanceFromHome
+
0.02
+
+
+
+
+
EmployeeNumber
+
-0.01
+
+
+
PercentSalaryHike
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
+
+
NumCompaniesWorked
+
0.16
+
+
+
+
StockOptionLevel
+
0.08
+
+
+
+
+
WorkLifeBalance
+
0.06
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
EducationField
+
0.05
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
+
EnvironmentSatisfaction
+
0.04
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
YearsSinceLastPromotion
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
YearsAtCompany
+
0.62
+
+
+
YearsInCurrentRole
+
0.55
+
+
+
YearsWithCurrManager
+
0.51
+
+
+
TotalWorkingYears
+
0.40
+
+
+
+
+
+
+
PercentSalaryHike
+
-0.02
+
+
+
DistanceFromHome
+
0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
+
NumCompaniesWorked
+
0.09
+
+
+
RelationshipSatisfaction
+
0.09
+
+
+
+
+
EducationField
+
0.05
+
+
+
StockOptionLevel
+
0.05
+
+
+
+
TrainingTimesLastYear
+
0.04
+
+
+
EnvironmentSatisfaction
+
0.03
+
+
+
BusinessTravel
+
0.03
+
+
+
+
JobInvolvement
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
YearsWithCurrManager
+
+
+
+
MISSING:
+
+ ---
+
+
+
+
+
+
+
+
+
+ Auto
+ 5
+ 15
+ 30
+
+
+
+
+
+
+
+
+ >
+
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1)
+
+
+
+
+
YearsAtCompany
+
0.77
+
+
+
YearsInCurrentRole
+
0.71
+
+
+
YearsSinceLastPromotion
+
0.51
+
+
+
TotalWorkingYears
+
0.46
+
+
+
+
+
+
+
+
DistanceFromHome
+
0.01
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1)
+
+
+
+
+
+
NumCompaniesWorked
+
0.16
+
+
+
+
+
StockOptionLevel
+
0.06
+
+
+
RelationshipSatisfaction
+
0.06
+
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
EducationField
+
0.05
+
+
+
+
JobSatisfaction
+
0.04
+
+
+
+
JobInvolvement
+
0.03
+
+
+
+
+
+
+
+
MOST FREQUENT VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SMALLEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LARGEST VALUES
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file