There are many techniques you can apply to improve the performance of your machine learning models, but two of the most powerful are model selection and hyperparameter tuning. As models...
There are loads of different ways to convert categorical variables into numeric features so they can be used within machine learning models. While you can perform this process manually on...
Selecting, filtering and subsetting data is probably the most common task you’ll undertake if you work with data. It allows you to extract subsets of data where row or column...
Machine learning models often take hours or days to run, especially on large datasets with many features. If your machine goes off, you’ll lose your model and you’ll need to...
When working with time series data, such as web analytics data or ecommerce sales, the time series format in your dataset might not be ideal for the analysis you’re performing...
If you regularly work with time series data in Pandas it’s probable that you’ll sometimes need to convert dates or datetimes and extract additional features from them.
Pandas allows you to import data from a wide range of data sources directly into a dataframe. These can be static files, such as CSV, TSV, fixed width files, Microsoft...
Transactional item data can be used to create a number of other useful datasets to help you analyse ecommerce products and customers. From the core list of items purchased you...
Time series forecasting models are notoriously tricky to master, especially in ecommerce, where you have seasonality, the weather, marketing promotions, and holidays to consider. Not to mention pandemics.
