EXPERIMENT 07: IMPLEMENTATION OF DECISION TREE REGRESSOR MODEL FOR PREDICTING THE SALARY OF THE EMPLOYEE
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 required packages and read the data file.
- Use LabelEncoder to convert categorical data into numerical data.
- Split data into testing data and training data.
- Apply Decision Tree Regressor.
- Calculate mean squared error and R2.
/*
Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
Developed by: SHAVEDHA.Y
RegisterNumber: 212221230095
*/
import pandas as pd
data=pd.read_csv("Salary.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]])
Thus the program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee is written and verified using python programming.