IBM人力资源分析员工流失&使用KNN

的绩效

原文:https://www . geesforgeks . org/IBM-HR-analytics-员工-自然减员-绩效-使用-knn/

减员是一个影响所有企业的问题，无论地理位置、行业和公司规模如何。对一个组织来说，这是一个重大问题，预测人员流动是许多组织人力资源需求的首要问题。组织面临着员工流动带来的巨大成本。随着机器学习和数据科学的进步，预测员工流失成为可能，我们将使用 KNN (k 近邻)算法进行预测。
数据集:
由 IBM 人力资源部发布的数据集在 Kaggle 提供。
数据集
代码:实现 KNN 算法进行分类。
加载库

Python 3

# 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


编码:导入数据集

Python 3

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


输出:

代码:数据集信息

Python 3

df.info()


输出:

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


代码:可视化数据

Python 3

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


输出:

因此，我们可以看到数据集中没有缺失值。
这是一个二元分类问题，因此实例在两个类中的分布如下图所示:

Python 3

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


输出:

代码:

Python 3

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


输出:

代码:

Python 3

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


输出:

数据预处理
数据集中有 4 个不相关的列，分别是:EmployeeCount、EmployeeNumber、Over18 和 StandardHour。所以，为了更准确，我们必须去掉这些。
T4【代码:

Python 3

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)


输出:

(1470, 31)


所以，我们删除了无关的栏目。
代码:输入输出数据

Python 3

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


代码:标签编码

Python 3

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


在数据集中有 7 个分类数据，因此我们必须将它们更改为 int 数据，也就是说，我们必须创建 7 个虚拟变量以提高准确性。
代码:虚拟变量创建

Python 3

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)


输出:

(1470, 49)
(1470, )


代码:拆分数据进行训练测试

Python 3

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)


预处理已经完成，现在我们必须对数据集应用 KNN。
模型执行代码:利用 KNeighborsClassifier，借助误分类错误找到最佳邻居数。

Python 3

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()


输出:

The optimal number of neighbors is 7


代码:预测得分

Python 3

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)


输出:

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