implementation-of-decision-tree-classifier-model-for-predicting-employee-churn's Introduction

Implementation-of-Decision-Tree-Classifier-Model-for-Predicting-Employee-Churn

AIM:

To write a program to implement the Decision Tree Classifier Model for Predicting Employee Churn.

Equipments Required:

Hardware – PCs
Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm

Import pandas
Import Decision tree classifier
Fit the data in the model
Find the accuracy score

Program:

/*
Program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
Developed by: Raja Lakshmi E
RegisterNumber: 212222220033 
*/

import pandas as pd
data=pd.read_csv("/content/Employee.csv")
data.head()
data.info()
data.head()
data.info()
data.isnull().sum()
data["left"].value_counts()
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
data["salary"]=le.fit_transform(data["salary"])
data.head()
x=data[["satisfaction_level","last_evaluation","number_project","average_montly_hours","time_spend_company","Work_accident","promotion_last_5years","salary"]]
x.head()
y=data["left"]
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=100)
from sklearn.tree import DecisionTreeClassifier
dt=DecisionTreeClassifier(criterion="entropy")
dt.fit(x_train,y_train)
y_pred=dt.predict(x_test)
from sklearn import metrics
accuracy=metrics.accuracy_score(y_test,y_pred)
accuracy
dt.predict([[0.5,0.8,9,260,6,0,1,2]])