Logistic Regression Model on Why HR Leaving | Predicting employee attrition using Machine Learning

Question

Predict retention of an employee within an organization such that whether the employee will leave the company or continue with it. An organization is only as good as its employees, and these people are the true source of its competitive advantage. Dataset is downloaded from Kaggle. Link: https://www.kaggle.com/giripujar/hr-analytics

First do data exploration and visualization, after this create a logistic regression model to predict Employee Attrition Using Machine Learning & Python.

Answer 1

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

from sklearn.linear_model import LogisticRegression

df = pd.read_csv("/content/HR_comma_sep.csv")

#df.head()

#Logistics Regression model

df1 = df[['salary','satisfaction_level', 

          'average_montly_hours', 

          'promotion_last_5years','left']]

dummies = pd.get_dummies(df1.salary)

df1 = pd.concat([df1,dummies],axis = 'columns')

df1 = df1.drop(['salary','medium'],axis='columns')

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(df1[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years','high','low']],df1.left, test_size =2/3,random_state = 1)

model = LogisticRegression()

model.fit(X_train,y_train)

model.score(X_test,y_test)

***************************** O U T P U T *****************************

0.7828

Answer 2

GO_STP_6266:

https://www.linkedin.com/posts/sahil-parmar-4099391bb_google-colaboratory-activity-6808080834747363328-aosT

Answer 3

#GO_STP_379

# In this task we have to find the students scores based on their study hours. 

# This is a simple Regression problem type because it has only two variables.

import pandas as pd

data = pd.read_csv('HR_comma_sep.csv')

# exploration of data

print("-------exploration of data------------")

print(data.info())

print(data.head())

# laber encoder of data

from sklearn.preprocessing import LabelEncoder

col=['Department','salary']

label_encoder =LabelEncoder()

data['Department']= label_encoder.fit_transform(data['Department'])

data['salary']= label_encoder.fit_transform(data['salary'])

print("after the laber encoder : \n",data)

# LogisticRegression of data

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.metrics import confusion_matrix,accuracy_score

ft=data[['Department','satisfaction_level','salary']]

label=data['left']

xtrain,xtest,ytrain,ytest=train_test_split(ft,label)

my_model=LogisticRegression()

my_model.fit(xtrain,ytrain)

y_pred=my_model.predict(xtest) # y test

cm=confusion_matrix(ytest,y_pred)

print("confusion matrix: ",cm)

print("accuracy socre: ",accuracy_score(ytest,y_pred))

print("socre: ",my_model.score(xtrain,ytrain))

 

# visualization of data

import matplotlib.pyplot as plt

plt.subplot(2,2,1)

plt.scatter(ytest, y_pred, marker = '+')

plt.xlabel('xtest')

plt.ylabel('y prediction')

plt.legend()

plt.title('Prediction of company')

plt.subplot(2,2,2)

plt.scatter(x=data['salary'], y=data['left'],label='salary and left')

plt.xlabel('x')

plt.ylabel('y')

plt.legend()

plt.title('salary and left')

plt.subplot(2,2,3)

plt.scatter(x=data['satisfaction_level'], y=data['left'],label='satisfaction level and left')

plt.xlabel('x')

plt.ylabel('y')

plt.legend()

plt.title('satisfaction level and left')

plt.subplot(2,2,4)

plt.scatter(x=data['time_spend_company'], y=data['left'],label='time_spend_company and left')

plt.xlabel('x')

plt.ylabel('y')

plt.title('time_spend_company and left')

plt.legend()

plt.show()

 

# logistic regression model to predict Employee Attrition

#create a pipeline for Logistic Regression

from sklearn.externals import joblib

import joblib as joblib

import pickle

with open('model_save','wb') as file:

    pickle.dump(my_model,file)

#load model and prediction

with open('model_save','rb') as file:

    newmodel=pickle.load(file)

# newmodel.coef_

joblib.dump(my_model,'model_joblib')

mymodel=joblib.load('model_joblib')

print("my model: ",mymodel)

print("new model: ",newmodel)

print("file is :",file)

Answer 4

https://www.linkedin.com/posts/shri-nivas_google-colaboratory-activity-6808318909477998592-gcBh