To write a program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
- Hardware โ PCs
- Anaconda โ Python 3.7 Installation / Jupyter notebook
- Import all the packages that helps to implement Decision Tree.
- Download and upload required csv file or dataset for predecting Employee Churn
- Initialize variables with required features.
- And implement Decision tree classifier to predict Employee Churn
/*
Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
Developed by: Elamaran
RegisterNumber: 212222040041
*/
import pandas as pd
data=pd.read_csv('/content/Salary_EX7.csv')
data.head()
data.info()
data.isnull().sum()
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
data['Position']=le.fit_transform(data['Position'])
data.head()
x=data[["Position","Level"]]
y=data["Salary"]
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=2)
from sklearn.tree import DecisionTreeRegressor
dt=DecisionTreeRegressor()
dt.fit(x_train,y_train)
y_pred=dt.predict(x_test)
from sklearn import metrics
mse=metrics.mean_squared_error(y_test,y_pred)
mse
r2=metrics.r2_score(y_test,y_pred)
r2
dt.predict([[5,6]])
plt.figure(figsize=(10,6))
plot_tree(dt,feature_names=x.columns,class_names=['Salary'], filled=True)
plt.show()
Thus the program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee is written and verified using python programming.