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 / Jupyter notebook
- Import the libraries and read the data frame using pandas.
- Calculate the null values present in the dataset and apply label encoder.
- Determine test and training data set and apply decison tree regression in dataset.
- Calculate Mean square error,data prediction and r2.
/*
Program to implement the Decision Tree Regressor Model for Predicting the Salary of the Employee.
Developed by: Prasanna R
RegisterNumber: 212222040120
*/
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"]]
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]])
from sklearn.tree import DecisionTreeRegressor, plot_tree
import matplotlib.pyplot as plt
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.