To write a program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
- Hardware – PCs
- Anaconda – Python 3.7 Installation / Jupyter notebook
- Import the standard libraries.
- Upload the dataset and check for any null values using .isnull() function.
- Import LabelEncoder and encode the dataset.
- Import DecisionTreeRegressor from sklearn and apply the model on the dataset.
- Predict the values of arrays.
- Import metrics from sklearn and calculate the MSE and R2 of the model on the dataset.
- Predict the values of array.
- Apply to new unknown values.
# Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
# Developed by: Sanjay Ragavendar M K
# RegisterNumber: 212222100045
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
y=data['Salary']
y
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 DecisionTreeClassifier,plot_tree
dt=DecisionTreeClassifier()
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
import matplotlib.pyplot as plt
dt.predict([[5,6]])
plt.figure(figsize=(20,8))
plot_tree(dt,feature_names=x.columns,filled=True)
plt.show()
Thus the program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee is written and verified using python programming.