pandas

In the last two posts, we've covered reading in the data set and handling missing values. Now we can start working on transforming the variable values into formatted features that our model can use. Different implementations of the Random Forest algorithm can accept different types of data. Scikit-learn requires everything to be numeric so we'll have to do some work to transform the raw data. All possible data can be generally considered as one of two types: Quantitative and Qualitative. Quantitative variables are those whose values can be meaningfully sorted in a manner that indicates an underlying order. In [...]

There will be missing/incorrect data in nearly every non-trivial data set a data scientist ever encounters. It is as certain as death and taxes. This is especially true with big data and applies to data generated by humans in a social context or by computer systems/sensors. Some predictive models inherently are able to deal with missing data (neural networks come to mind) and others require that the missing values be dealt with separately. The RandomForestClassifier model in scikit-learn is not able to handle missing values, so we'll need to use some different approaches to assign values before training the [...]