Project 2: Feature Selection in machine learning using the Pearson Correlation.
Feature selection is a crucial step in machine learning that involves selecting a subset of relevant features from the original set of input features to build a more efficient and accurate predictive model. One common approach for feature selection is using the Pearson correlation coefficient to assess the relationship between each feature and the target variable.
The Pearson correlation coefficient, often denoted by "r," measures the linear relationship between two continuous variables. It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no linear correlation, and 1 indicates a perfect positive correlation.
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