implementation-of-decision-tree-regressor-model-for-predicting-the-salary-of-the-employee's Introduction

Implementation-of-Decision-Tree-Regressor-Model-for-Predicting-the-Salary-of-the-Employee

AIM:

To write a program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.

Equipments Required:

Hardware – PCs
Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm

Step1:

Import the required libraries.

Step2:

Upload the csv file and read the dataset.

Step3:

Check for any null values using the isnull() function.

Step4:

From sklearn.tree inport DecisionTreeRegressor.

Step5:

Import metrics and calculate the Mean squared error.

Step6:

Apply metrics to the dataset, and predict the output.

Program:

/* Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.

Developed by: Akash A

import pandas as pd
data=pd.read_csv("Salary.csv")
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]])

Output:

Initial Dataset:

Data Info:

Encoded Dataset:

MSE Value:

R Squared Value:

Data Prediction:

Result:

Thus the program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee is written and verified using python programming.

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