Author(s): Shahidullah Kawsar

Originally published on Towards AI.

Data Scientist & Machine Learning Interview Preparation

Data preparation is the foundation of every successful machine learning project. Before algorithms can learn, raw data must be collected, cleaned, understood, and transformed into a form that models can use effectively. This process involves handling missing values, reducing noise, engineering meaningful features, and ensuring data quality and consistency. In this blog, we’ll explore why data preparation matters, the key steps involved, and best practices that help turn messy data into a strong, reliable input for building accurate, robust, and scalable machine learning models.

In the article, various aspects of data preparation for machine learning are discussed, outlining fundamental concepts such as handling missing values, filtering outliers, and the importance of feature engineering techniques like normalization and one-hot encoding. Practical interview questions related to data preparation are presented, allowing readers to test their knowledge and prepare effectively for data science interviews. Techniques such as dimensionality reduction, careful preprocessing, and addressing issues of concept drift and target leakage are highlighted to improve model accuracy and generalization.

Read the full blog for free on Medium.

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Published via Towards AI