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 import 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: Santhosh M
RegisterNumber: 212220040146
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]])
*/
DATA HEAD
DATA INFO
Data Head after applying LabelEncoder():
MSE:
r2:
DATA PREDICTION:
Thus the program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee is written and verified using python programming.