Activating python-kernel for an environment in jupyter-notebook

When you start a notebook after installing a new environment, sometimes you will not find your environment-kernel in the dropdown from the notebook.

To fix this, install ipykernel on to your desired environment. Here, we install it into new_env

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Handling missing values in a Dataset before training

How to impute missing values in a dataset before feeding to a classifier is often a difficult decision. Imputing with a wrong value can significantly skew the data and result in wrong classifier. The ideal solution is to get a clean data set without any NULL values but then, we might have to throw out most data. There are no perfect workarounds as most classifiers are built based on the information from data and lack thereof results in the wrong classifier. Continue reading “Handling missing values in a Dataset before training”