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 / Moodle-Code Runner
- Import the required libraries.
- Upload the csv file and read the dataset.
- Check for any null values using the isnull() function.
- From sklearn.tree inport DecisionTreeRegressor.
- Import metrics and calculate the Mean squared error.
- Apply metrics to the dataset, and predict the output.
/*
Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
Developed by: Swathika.G
RegisterNumber: 212221230113
*/
import pandas as pd
data=pd.read_csv("/content/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"]]
x.head()
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.