In Poisson regression, we assume that our Y variable is Poisson distributed. This means that we assume that each row in the dataset is Poisson distributed with its unique expected value.

In this article we’ll come up with the line of best fit for linear regression using the least squares equation as well as the normal equation from linear algebra. While these methods are inter-connected, it is helpful to walk through the logic enshrined in the approaches. We’ll use SKLearn’s demo dataset ‘Boston’ for this purpose. We’ll then compare our values with SKLearn’s inbuilt LinearRegression class.

Data Science Problem:

My project’s first objective was to predict the number of deaths from diseases related to air pollution: namely Ischaemic Heart Disease (IHD) and Chronic Obstructive Pulmonary Disease (COPD). The second objective was to isolate and quantify the effect of air pollution in those deaths. I decided to structure my problem so that I was predicting deaths for each five year age group (for example, 65-70 years) for each county in the continental United States.

This study looks at the relationship of different variables on attendance rates in NYC public high schools, and attempts to extract the effect of school safety in particular. Both school safety. as well as school attendance rates are both a critical focus for schools adiminstation entities.

It is understood that there are significant distinctions between the stylistic makeup Shakespearean tragedies and comedies. Tragedies typlically have a cental flawed figure, and the general sentiment of tragedies ebb and flow according to the machinations of that central figure.

This analysis is on using Natural Language Processing to identify character arcs in Shakespeare’s plays, as well as identify broad thematic sentiments/polarity of the plays themselves.

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