Getting Started with Linear Regression in Python: A Beginner’s Guide

Unlock the power of predictive modeling with this step-by-step guide to implementing linear regression in Python

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Linear regression is a statistical method that is used to model the relationship between a dependent variable and one or more independent variables. In machine learning, linear regression is often used as a simple but powerful tool for predicting a target value based on input features. In this article, we will guide you through the implementation of linear regression in Python, step by step.

Let’s begin.

The first step is to install the required libraries. In this case, we will be using the popular machine learning library scikit-learn and the data manipulation library pandas. You can install these libraries by running the following command in your terminal:

pip install scikit-learn pandas

Next, we will import the necessary modules into our Python script:

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error