The Potential of Machine Learning in Handling Missing Data: Balancing Trade-Offs for Small Projects

     


Handling missing data is a significant challenge for analysts in their projects. Despite the availability of various techniques, such as omitting fields with missing data or imputing the missing values, ensuring accurate results from the curated data requires careful consideration. While analysts choose techniques based on project requirements, it is difficult to guarantee that they will yield the best possible results with the given dataset.

 

In many projects, analysts may have limitations in terms of time, resources, or the ability to explore multiple methods for handling missing data. Due to these constraints, they are often forced to select the best model among the few methods they were able to try. However, this limited bandwidth raises the concern of potentially missing out on better solutions.

 

In my personal experience, I find that Machine Learning-Based Methods have the potential to overcome the limitations of many conventional techniques in terms of accuracy when handling missing data. These methods utilize advanced algorithms and models to predict and impute missing values based on patterns and relationships in the data. However, it's important to acknowledge that effectively implementing machine learning-based methods requires a significant investment of time and skill, particularly in model development as developing an accurate machine learning model requires thorough understanding of the data, feature engineering, selection of appropriate algorithms, hyperparameter tuning, and robust validation techniques.

 

While ML based techniques can enhance the results delivered by an analyst do you think it’s worth trade offs in terms of increased skill, time and capital in all cases? How can we justify this for a small project?

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