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
- Load the dataset and preprocess it, including encoding categorical variables.
- Split the dataset into features (X) and the target variable (y), then split them into training and testing sets.
- Initialize and train a DecisionTreeClassifier model with entropy criterion on the training data.
- Evaluate the model's accuracy on the test data and make predictions on new data points.
/*
Program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
Developed by: HARI PASATH.R.K
RegisterNumber: 212223040055
*/
import pandas as pd
data = pd.read_csv("/Employee_EX6 (2).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
print(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.