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IBM HR Analytics Employee Attrition & Performance using KNN
  • Last Updated : 11 May, 2020
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Attrition is a problem that impacts all businesses, irrespective of geography, industry and size of the company. It is a major problem to an organization, and predicting turnover is at the forefront of the needs of Human Resources (HR) in many organizations. Organizations face huge costs resulting from employee turnover. With advances in machine learning and data science, it’s possible to predict the employee attrition and we will predict using KNN (k-nearest neighbours) algorithm.

Dataset:
The dataset that is published by the Human Resource department of IBM is made available at Kaggle.
dataset

Code: Implementation of KNN algorithm for classification.

Loading the Libraries




# performing linear algebra
import numpy as np 
  
# data processing
import pandas as pd
  
# visualisation
import matplotlib.pyplot as plt
import seaborn as sns % matplotlib inline

Code: Importing the dataset






dataset = pd.read_csv("WA_Fn-UseC_-HR-Employee-Attrition.csv")
print (dataset.head)

Output :

Code: Information about the dataset




df.info()

Output :


RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
Age                         1470 non-null int64
Attrition                   1470 non-null object
BusinessTravel              1470 non-null object
DailyRate                   1470 non-null int64
Department                  1470 non-null object
DistanceFromHome            1470 non-null int64
Education                   1470 non-null int64
EducationField              1470 non-null object
EmployeeCount               1470 non-null int64
EmployeeNumber              1470 non-null int64
EnvironmentSatisfaction     1470 non-null int64
Gender                      1470 non-null object
HourlyRate                  1470 non-null int64
JobInvolvement              1470 non-null int64
JobLevel                    1470 non-null int64
JobRole                     1470 non-null object
JobSatisfaction             1470 non-null int64
MaritalStatus               1470 non-null object
MonthlyIncome               1470 non-null int64
MonthlyRate                 1470 non-null int64
NumCompaniesWorked          1470 non-null int64
Over18                      1470 non-null object
OverTime                    1470 non-null object
PercentSalaryHike           1470 non-null int64
PerformanceRating           1470 non-null int64
RelationshipSatisfaction    1470 non-null int64
StandardHours               1470 non-null int64
StockOptionLevel            1470 non-null int64
TotalWorkingYears           1470 non-null int64
TrainingTimesLastYear       1470 non-null int64
WorkLifeBalance             1470 non-null int64
YearsAtCompany              1470 non-null int64
YearsInCurrentRole          1470 non-null int64
YearsSinceLastPromotion     1470 non-null int64
YearsWithCurrManager        1470 non-null int64
dtypes: int64(26), object(9)
memory usage: 402.0+ KB

Cdoe: Visualizing the data




# heatmap to check the missing value
plt.figure(figsize =(10, 4))
sns.heatmap(dataset.isnull(), yticklabels = False, cbar = False, cmap ='viridis')

Output:


So, we can see that there are no missing values in the dataset.
This is a Binary Classification Problem, so the Distribution of instances among the 2 classes, is visualized below:




sns.set_style('darkgrid')
sns.countplot(x ='Attrition', data = dataset)

Output:

Code:






sns.lmplot(x = 'Age', y = 'DailyRate', hue = 'Attrition', data = dataset) 

Output:


Code :




plt.figure(figsize =(10, 6))
sns.boxplot(y ='MonthlyIncome', x ='Attrition', data = dataset)

Output:


Preprocessing the data
In the dataset there are 4 irrelevant columns, i.e:EmployeeCount, EmployeeNumber, Over18 and StandardHour. So, we have to remove these for more accuracy.
Code:




dataset.drop('EmployeeCount', axis = 1, inplace = True)
dataset.drop('StandardHours', axis = 1, inplace = True)
dataset.drop('EmployeeNumber', axis = 1, inplace = True)
dataset.drop('Over18', axis = 1, inplace = True)
print(dataset.shape)

Output:

(1470, 31)

So, we have removed the irrelevant column.

Code: Input and Output data




y = dataset.iloc[:, 1]
X = dataset
X.drop('Attrition', axis = 1, inplace = True)

Code: Label Encoding




from sklearn.preprocessing import LabelEncoder
lb = LabelEncoder()
y = lb.fit_transform(y)

In the dataset there are 7 categorical data, so we have to change them to int data, i.e we hava to create 7 dummy variable for better accuracy.

Code: Dummy variable creation




dum_BusinessTravel = pd.get_dummies(dataset['BusinessTravel'], 
                                    prefix ='BusinessTravel')
dum_Department = pd.get_dummies(dataset['Department'], 
                                prefix ='Department')
dum_EducationField = pd.get_dummies(dataset['EducationField'], 
                                    prefix ='EducationField')
dum_Gender = pd.get_dummies(dataset['Gender'], 
                            prefix ='Gender', drop_first = True)
dum_JobRole = pd.get_dummies(dataset['JobRole'], 
                             prefix ='JobRole')
dum_MaritalStatus = pd.get_dummies(dataset['MaritalStatus'], 
                                   prefix ='MaritalStatus')
dum_OverTime = pd.get_dummies(dataset['OverTime'], 
                              prefix ='OverTime', drop_first = True)
# Adding these dummy variable to input X
X = pd.concat([x, dum_BusinessTravel, dum_Department, 
               dum_EducationField, dum_Gender, dum_JobRole, 
               dum_MaritalStatus, dum_OverTime], axis = 1)
# Removing the categorical data
X.drop(['BusinessTravel', 'Department', 'EducationField'
        'Gender', 'JobRole', 'MaritalStatus', 'OverTime'], 
        axis = 1, inplace = True)
  
print(X.shape)
print(y.shape)

Output:

(1470, 49)
(1470, )

Code: Splitting data to training and testing




from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size = 0.25, random_state = 40)

So, the preprocessing is done, now we have to apply KNN to the dataset.
Model Execution code: Using KNeighborsClassifier for finding the best number of neighbour with the help of misclassification error.




from sklearn.neighbors import KNeighborsClassifier
neighbors = [] 
cv_scores = [] 
    
from sklearn.model_selection import cross_val_score 
# perform 10 fold cross validation 
for k in range(1, 40, 2): 
    neighbors.append(k) 
    knn = KNeighborsClassifier(n_neighbors = k) 
    scores = cross_val_score( 
        knn, X_train, y_train, cv = 10, scoring = 'accuracy'
    cv_scores.append(scores.mean())
error_rate = [1-x for x in cv_scores] 
    
# determining the best k 
optimal_k = neighbors[error_rate.index(min(error_rate))] 
print('The optimal number of neighbors is % d ' % optimal_k) 
    
# plot misclassification error versus k 
plt.figure(figsize = (10, 6)) 
plt.plot(range(1, 40, 2), error_rate, color ='blue', linestyle ='dashed', marker ='o',
         markerfacecolor ='red', markersize = 10)
plt.xlabel('Number of neighbors'
plt.ylabel('Misclassification Error'
plt.show() 

Output:

The optimal number of neighbors is  7 

Code: Prediction Score




from sklearn.model_selection import cross_val_predict, cross_val_score
from sklearn.metrics import accuracy_score, classification_report
from sklearn.metrics import confusion_matrix
  
def print_score(clf, X_train, y_train, X_test, y_test, train = True):
    if train:
        print("Train Result:")
        print("------------")
        print("Classification Report: \n {}\n".format(classification_report(
                y_train, clf.predict(X_train))))
        print("Confusion Matrix: \n {}\n".format(confusion_matrix(
                y_train, clf.predict(X_train))))
  
        res = cross_val_score(clf, X_train, y_train, 
                              cv = 10, scoring ='accuracy')
        print("Average Accuracy: \t {0:.4f}".format(np.mean(res)))
        print("Accuracy SD: \t\t {0:.4f}".format(np.std(res)))
        print("accuracy score: {0:.4f}\n".format(accuracy_score(
                y_train, clf.predict(X_train))))
        print("----------------------------------------------------------")
                 
    elif train == False:
        print("Test Result:")
        print("-----------")
        print("Classification Report: \n {}\n".format(
                classification_report(y_test, clf.predict(X_test))))
        print("Confusion Matrix: \n {}\n".format(
                confusion_matrix(y_test, clf.predict(X_test)))) 
        print("accuracy score: {0:.4f}\n".format(
                accuracy_score(y_test, clf.predict(X_test))))
        print("-----------------------------------------------------------")
          
knn = KNeighborsClassifier(n_neighbors = 7)
knn.fit(X_train, y_train)
print_score(knn, X_train, y_train, X_test, y_test, train = True)
print_score(knn, X_train, y_train, X_test, y_test, train = False)

Output:

Train Result:
------------
Classification Report: 
               precision    recall  f1-score   support

           0       0.86      0.99      0.92       922
           1       0.83      0.19      0.32       180

    accuracy                           0.86      1102
   macro avg       0.85      0.59      0.62      1102
weighted avg       0.86      0.86      0.82      1102


Confusion Matrix: 
 [[915   7]
 [145  35]]

Average Accuracy:      0.8421
Accuracy SD:          0.0148
accuracy score: 0.8621

-----------------------------------------------------------
Test Result:
-----------
Classification Report: 
               precision    recall  f1-score   support

           0       0.84      0.96      0.90       311
           1       0.14      0.04      0.06        57

    accuracy                           0.82       368
   macro avg       0.49      0.50      0.48       368
weighted avg       0.74      0.82      0.77       368


Confusion Matrix: 
 [[299  12]
 [ 55   2]]

accuracy score: 0.8179

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