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 standard Libraries.
- Import LabelEncoder and fit_transform "Salary".
- Assign values for x and y.
- Import test_train_split from sklearn and assign values.
- Import DecisionTreeClassifier and predict x_test
- Find the accuracy and predict the given values.
/*
Program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
Developed by: Iniyan S
RegisterNumber: 212220040053
*/
import pandas as pd
data = pd.read_csv("/content/sample_data/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.