Understanding the Fixed Effects Regression Model

And a Python tutorial on how to build and train a Fixed Effects model on a real-world panel data set

The Fixed Effects regression model is used to estimate the effect of intrinsic characteristics of individuals in a panel data set. Examples of such intrinsic characteristics are genetics, acumen and cultural factors. Such factors are not directly observable or measurable but one needs to find a way to estimate their effects since leaving them out leads to a sub-optimally …

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