To write a program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
- Hardware โ PCs
- Anaconda โ Python 3.7 Installation / Moodle-Code Runner
- Import the required libraries.
- Upload and read the dataset.
- Gather information and presence of null in the dataset
- From sklearn.tree import DecisionTreeClassifier and use criterion as entropy.
- Find the accuracy score of the model.
- Check the trained model.
/*
Program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
Developed by: Pradeesh S
RegisterNumber: 212221240038
*/
#import packages
import pandas as pd
df=pd.read_csv("Employee.csv")
df.head()
#checking the data information and null presence
df.info()
df.isnull().sum()
#checking the count of left column
df["left"].value_counts()
#encoding categorical features to numeric
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
df["salary"]=le.fit_transform(df["salary"])
df.head()
#assigning x and y array and display them
x=df[["satisfaction_level","last_evaluation","number_project",
"average_montly_hours","time_spend_company","Work_accident",
"promotion_last_5years","salary"]]
x.head()
y=df["left"]
y
#splitting data into training and test
#implementing decision tree classifier in training model
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)
#calcuating accuracy score
from sklearn import metrics
acc=metrics.accuracy_score(y_test,y_pred)
acc
#testing the model
dt.predict([[.5,.8,9,260,6,0,1,2]])
Thus the program to implement the Decision Tree Classifier Model for Predicting Employee Churn is written and verified using python programming.