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.
- Check for any null values using the isnull() function.
- From sklearn.tree import DecisionTreeClassifier and use criterion as entropy.
- Find the accuracy of the model and predict the required values by importing the required module from sklearn.
/*
Program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
Developed by: Swathika G
RegisterNumber: 212221230113
*/
import pandas as pd
data=pd.read_csv("/content/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"]]
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]])
Thus the program to implement the Decision Tree Classifier Model for Predicting Employee Churn is written and verified using python programming.