/*
Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
Developed by: Vineela Shaik
RegisterNumber:212223040243

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"]]
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]])

*/