PCA Insights is a data analysis project aimed at applying Principal Component Analysis (PCA) to high-dimensional datasets for dimensionality reduction, visualization, and exploration.

High-dimensional loan transaction data. By reducing the dimensionality of the dataset, patterns were identified to help a financial institution mitigate risks such as loan defaults or early repayments. Key steps include data preprocessing, PCA implementation, and interpretation of principal components to uncover significant insights.

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Data science project applying feature selection/dimensionality reduction techniques to identify the explanatory variables to be included within a linear regression model that predicts the number of times an online news article will be shared using Python 3 in a Juypter Notebook.