Career Coaching for Data Professionals
Building trustworthy data pipelines because AI cannot learn from dirty data
Using scikit-automl for building a classification model
My first attempt to use scikit-automl and how I got it working
29 Mar 2019
How to return rows with missing values in Pandas DataFrame
How does it work and why the most popular solution is wrong
27 Mar 2019
Preprocessing the input Pandas DataFrame using ColumnTransformer in Scikit-learn
How to encode text/categorical variables and scale numerical values using only one Scikit-learn class
25 Mar 2019
How to install scikit-automl in a Kaggle notebook
error: command ‘swig’ failed with exit status 1 while installing scikit-automl
22 Mar 2019
Predicting customer lifetime value using the Pareto/NBD model and Gamma-Gamma model
How to estimate the CLV from a list of customer transactions using the lifetimes library in Python
20 Mar 2019
Predicting customer churn using the Pareto/NBD model
How to use a Python lifetimes library to build a Pareto/NBD model.
18 Mar 2019
Business metrics that make no sense
There are three kinds of metrics that won’t destroy your business.
15 Mar 2019
Nested cross-validation in time series forecasting using Scikit-learn and Statsmodels
Tweaking the parameters of Statsmodels
13 Mar 2019
How to perform an A/B test correctly in Python
What can we expect from a correctly performed A/B test?
11 Mar 2019
[book review] The hundred-page machine learning book
I have mixed feelings about this book.
08 Mar 2019
A few useful things to know about machine learning
Pedro Domingo’s observations about feature engineering
06 Mar 2019
Recommendations vs. raw data — what is better?
Should we suggest an action when we visualize data?
04 Mar 2019