Random forest regression with Python

Random forest regression is a powerful machine learning technique that can be used for predicting continuous values. It works by constructing multiple decision trees during training and outputting the average prediction of the individual trees. First, we need to import the necessary libraries and load the dataset. Next, we split the data into training and testing sets. 
# Step 1: Import the necessary libraries and load the dataset
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

data = pd.read_csv('dataset.csv')

# Step 2: Split the data into features and target variable
X = data.drop('target_variable', axis=1)
y = data['target_variable']

# Step 3: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 4: Initialize and train the random forest regression model
rf_model = RandomForestRegressor()
rf_model.fit(X_train, y_train)

# Step 5: Make predictions on the testing set and calculate the mean squared error
predictions = rf_model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print('Mean Squared Error:', mse)

By following these steps, you can easily build and train a random forest regression model in Python. Feel free to adjust the hyperparameters of the random forest model to improve its performance on your dataset.

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