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lr.html
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<!DOCTYPE html>
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<title>Linear Regression</title>
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<h2>Linear Regression</h2>
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<h2 class="project-title">Linear Regression</h2>
<span class="project-subtitle">Supervised Learning</span>
<p>
A dependent variable and one or more independent variables are modeled using the statistical technique of linear regression. It makes the assumption that the dependent variable (outcome) and the independent variables (predictors) have a linear relationship.
The best-fitting straight line that minimizes the gap between the values predicted by the linear model and the observed values is the target of linear regression. Usually, this is accomplished by a technique called ordinary least squares (OLS) regression, which involves minimizing the sum of the squared differences between the observed and predicted values.
Many disciplines, including economics, finance, engineering, and the social sciences, use linear regression extensively for inference and prediction.
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<p>
<strong> Data Preparation </strong>
Labeled data is necessary for Linear Regression. This implies that a label identifying the class or category of each data point is required. To evaluate how well the trained model generalizes to new data, data is divided into training and testing sets.
Training Set: The model's training or construction set. It makes up the bulk of the information.
Testing Set: Applied to assess how well the trained model performs. It needs to be kept apart from the training set in order to provide an objective evaluation of the model's accuracy.
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<p><a href="https://github.com/sasibonu/sasibonu.github.io/blob/main/SasiBonuA05.ipynb">Code Link</a></p>
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