Random forest algorithm

1.  It is used in machine learning algorithm

2. It combines the output of multiple decision trees to reach a single result

3.  It’s handles both Classification and regression problems in a data

Python program to data set using random forest algorithm

Here I am taking the data set in a xlsx sheet. I am applying the random forest algorithm on that data.

Python program

# Importing the libraries
import numpy as np # for array operations
import pandas as pd # for working with DataFrames
import requests, io # for HTTP requests and I/O commands
import matplotlib.pyplot as plt # for data visualization
%matplotlib inline

# scikit-learn modules
from sklearn.model_selection import train_test_split # for splitting the data
from sklearn.metrics import mean_squared_error # for calculating the cost function
from sklearn.ensemble import RandomForestRegressor # for building the model

# Importing the dataset from the url of the data set
url = "https://drive.google.com/u/0/uc?id=1mVmGNx6cbfvRHC_DvF12ZL3wGLSHD9f_&export=download"
data = requests.get(url).content

# Reading the data
dataset = pd.read_csv(io.StringIO(data.decode('utf-8')))
dataset.head()

x = dataset.drop('Petrol_Consumption', axis = 1) # Features
y = dataset['Petrol_Consumption']  # Target

# Splitting the dataset into training and testing set (80/20)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 28)

# Initializing the Random Forest Regression model with 10 decision trees
model = RandomForestRegressor(n_estimators = 10, random_state = 0)

# Fitting the Random Forest Regression model to the data
model.fit(x_train, y_train) 

# Predicting the target values of the test set
y_pred = model.predict(x_test)

# RMSE (Root Mean Square Error)
rmse = float(format(np.sqrt(mean_squared_error(y_test, y_pred)),'.3f'))
print("\nRMSE:\n",rmse)

 

Output :-

Root Mean Square Error (RMSE):96.389

Conclusion:-

This RMSE value gives you an idea of how well the model is performing. The lower the RMSE, the better the model’s predictions are in terms of matching the actual values.