To write a program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
- Hardware โ PCs
- Anaconda โ Python 3.7 Installation / Jupyter notebook
- 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 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]])
Thus, the program to implement the Decision Tree Classifier Model for Predicting Employee Churn is written and verified using python programming.