The latest articles on DEV Community by sukriti-chowdhury (@sukritichowdhury). https://dev.to/sukritichowdhury https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3556883%2F570422a4-01a8-4663-8c0e-368de15f3bf4.png https://dev.to/sukritichowdhury en sukriti-chowdhury Fri, 10 Oct 2025 00:19:58 +0000 https://dev.to/sukritichowdhury/whatwhyhow-machine-learning-series-linear-regression-model-2fgo https://dev.to/sukritichowdhury/whatwhyhow-machine-learning-series-linear-regression-model-2fgo <h2> WHAT is a Linear Regression Model </h2> <p>Linear regression is a statistical method for modelling the linear relationship between a dependent variable and one or more independent variable.<br> For example, you want to predict house rent estimate (dependent variable), based on the collected data of multiple features (independent variable) such as house type, location, total area etc.</p> <p>• Used to predict numeric target (dependent variable).<br> • <strong>Mathematical Form:</strong> y = β0 + β1 * x1 + β2 * x2 + … + βn * xn + ε<br> • <strong>Features/Independent Variables:</strong> X = x1, x2, x3 … xn (House type, location, total area etc.)<br> • <strong>Target/Dependent Variables:</strong> y i.e. house rent estimate<br> • <strong>Goal:</strong> Goal of the model is to find the best fit coefficients (β0, β1, β, ...βn)<br> • <strong>Assumption:</strong> There might always be some error/residuals ε while predicting the target. Our end goal will be to tune and reduce the errors for the predictions.</p> <h2> WHY do we use Linear Regression Model </h2> <ul> <li><p><strong>Easy to implement and train:</strong> Linear regression is one of the simplest and most computationally efficient machine learning algorithms. Its straightforward mathematical foundation makes it quick to train, even on large datasets.</p></li> <li><p><strong>Excellent baseline model:</strong> Because of its simplicity, linear regression is often the first model to be used on a regression problem. It establishes a baseline performance that more complex algorithms can be measured against.</p></li> <li><p><strong>Limitations:</strong></p></li> </ul> <ol> <li>Too simple for complex business cases since it assumes linear relationship.</li> <li>Prone to overfitting due to it's simplicity.</li> <li>Sensitive to outliers.</li> </ol> machinelearning webdev development