dimensionalityreduction

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.

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.

here is where I practice data analysis and machine learning