Supervised Learning – Linear Regression
In supervised learning, a machine learning algorithm is trained on a labeled dataset. Linear regression is one of the most common techniques used in supervised learning for predicting continuous values.
To code linear regression in Python, you can use libraries such as scikit-learn or TensorFlow. Here is a simple example of coding linear regression using scikit-learn:
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Create a linear regression model
model = LinearRegression()
# Split the dataset 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)
# Fit the model on the training data
model.fit(X_train, y_train)
# Make predictions on the testing data
y_pred = model.predict(X_test)
This code snippet creates a linear regression model, splits the dataset into training and testing sets, fits the model on the training data, and makes predictions on the testing data. You can then evaluate the model’s performance using metrics such as mean squared error or R-squared.
Linear regression is a powerful technique for predicting continuous values and is widely used in various fields such as finance, economics, and healthcare. By coding linear regression in Python, you can harness the power of machine learning to make accurate predictions based on data.
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