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Embeddings: Applying Them to Tabular and Time Series Models
7 min readNov 30, 2024
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Imagine a retail store that wants to predict how much a customer is likely to spend next month. The data at hand includes various factors: customer demographics, purchase history, time spent on the website, and even seasonal trends.
To make accurate predictions, it’s essential to represent this data in a format that a machine learning model can efficiently process and learn from. This is where embeddings come in. They convert complex, high-dimensional data into compact, numerical representations, making it easier for models to find patterns and make predictions.
