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Python Advanced Coding Interview Questions Answers and Explanations
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.
In this article, we’ll explore what embeddings are, why they are useful, and how they can be applied to tabular data and time series models. By the end, you’ll have a clear understanding of their importance and see a practical Python implementation to get started.
What Are Embeddings?
Embeddings are numerical representations of data, typically in a lower-dimensional vector space. Originally popularized in natural language processing (NLP) tasks to represent words (e.g., Word2Vec, GloVe), embeddings have since found applications in diverse domains, including tabular and time series data.
