Multiple Linear Regression to Predict Consumer Spending

As in the last post, here's some more work in excel with economic variables. This time I use value forecasts of 30y mortgage, unemployment, and personal income rates, figured in a similar manner as before (annual growth/change rates - 10y moving averages) to predict future levels of personal consumption expenditures. I run a multilinear regression analysis to forecast PCE based upon the three independent variables and end up with some pretty strong results and an adjusted R-squared of .974.

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Practice at Dimensionality Reduction with SKlearn

I found some free time and thought I'd finally get some more practice at dimensionality reduction. With this goal in mind, I went onto Kaggle and found a competition(Estimate house prices) which looked appropriate to practice these skill with. Throughout this post I walk through the steps I took from cleaning and standardizing the data, to finally performing PCA and fitting a simple linear regression to the top five most influential eigenvectors! Not the most accurate regression ever, but great practice and surprisingly efficient given it drops 81 variables into only 5.

Predicting a Country's Happiness

A look at the world happiness index and an evaluation of the factors that contribute to the general happiness of a countries population. Variables were primarily focused on economic, political, and elegantarian factors. Originally conducted as a project for my SNHU Applied Stats II class. As it ended at around 30 pages, included is only a subset of the full project.

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