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

EXPERIMENT 06: 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.

EQUIPMENT'S REQUIRED:

Hardware – PCs
Anaconda – Python 3.7 Installation / Jupyter notebook

ALGORITHM:

Import required packages and read the data file.
Use LabelEncoder to convert categorical data into numerical data.
Split data into training set and testing set,
Predict Y values.
Calculate accuracy of the model.

PROGRAM:

/*
Program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
Developed by: SHAVEDHA.Y
Register Number: 212221230095  
*/

import pandas as pd
data=pd.read_csv("Employee.csv")

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"]]
data.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]])