Consulting
Speaker
Bartosz Mikulski
I help data engineering tech leads #makeDataTrustworthy because AI cannot learn from dirty data
All Stories
Guidelines for data science teams — a summary of Daniel Molnar’s talks
Avoiding over-engineering in machine learning
Ludwig machine learing model in Kaggle
My first attempt to use Ludwig
The problem of large categorical variables in machine learning
How to use FeatureHasher in Scikit-learn
Encoding categorical variables in machine learning
One-hot encoding, dummy coding, and effect coding in Scikit learn and Pandas
How To Avoid Data Leakage While Building A Machine Learning Model
What to do when your model works perfectly during testing but fails in production
Using scikit-automl for building a classification model
My first attempt to use scikit-automl and how I got it working
How to return rows with missing values in Pandas DataFrame
How does it work and why the most popular solution is wrong
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
How to install scikit-automl in a Kaggle notebook
error: command ‘swig’ failed with exit status 1 while installing scikit-automl
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
Predicting customer churn using the Pareto/NBD model
How to use a Python lifetimes library to build a Pareto/NBD model.
Business metrics that make no sense
There are three kinds of metrics that won’t destroy your business.
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