To write a program to implement the Decision Tree Classifier Model for Predicting Employee Churn.
- Hardware – PCs
- Anaconda – Python 3.7 Installation / Jupyter notebook
- 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: Palleri Yogi
RegisterNumber: 212220040108
*/
import pandas as pd
data = pd.read_csv('/content/Employee[1].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()
data["Departments "] = le.fit_transform(data["Departments "])
data.head()
data.info()
x = data[["satisfaction_level" ,"last_evaluation", "number_project", "average_montly_hours", "time_spend_company", "Work_accident", "promotion_last_5years", "Departments ", "salary"]]
x.info()
y = data[["left"]]
y.info()
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)
x_train.shape
y_train.shape
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = "entropy")
dt.fit(x_train, y_train)
y_pred = dt.predict(x_test)
y_pred
from sklearn import metrics
accuracy = metrics.accuracy_score(y_test,y_pred)
accuracy
dt.predict([[]])
Data Head:
Dataset Info:
Null dataset:
Value counts in left column:
Dataset transformed Head:
x.head:
Accuracy:
Data Prediction:
Thus the program to implement the Decision Tree Classifier Model for Predicting Employee Churn is written and verified using python programming.