What techniques do you use to detect and address feature correlation and multicollinearity during exploratory data analysis (EDA) to ensure model performance and interpretability?
What techniques do you use to detect and address feature correlation and multicollinearity during exploratory data analysis (EDA) to ensure model performance and interpretability?
I want to know how you ensure that complex technical data, analyses, or results are presented in a way that someone without a technical background can quickly grasp. It’s about your choices regarding layout, language, visualizations, summaries, and storytelling, all aimed at making the report “self-explanatory,” meaning it should largely make sense even without someone(Read More)
I want to know how you ensure that complex technical data, analyses, or results are presented in a way that someone without a technical background can quickly grasp. It’s about your choices regarding layout, language, visualizations, summaries, and storytelling, all aimed at making the report “self-explanatory,” meaning it should largely make sense even without someone needing to explain it in person. I am likely interested in understanding how you simplify complex information, how much you prioritize user-friendly design, and how you balance technical accuracy with accessibility. Also share the tools or techniques you use (like charts, dashboards, or annotations) and how you tailor your reports based on the audience’s needs.
In a world flooded with dashboards and data charts, not all visualizations lead to action. Sometimes, they look good but don’t help decision-makers understand what to do next. That’s why I’m curious, when you create or evaluate data visualizations, what frameworks or methods do you rely on to make sure they’re not just informative, but(Read More)
In a world flooded with dashboards and data charts, not all visualizations lead to action. Sometimes, they look good but don’t help decision-makers understand what to do next. That’s why I’m curious, when you create or evaluate data visualizations, what frameworks or methods do you rely on to make sure they’re not just informative, but actually actionable?
Data mining is often described as the process of discovering patterns, correlations, and trends within large datasets to generate actionable insights. But in today’s context—where data is abundant and growing exponentially—how do we ensure that the patterns we uncover are truly meaningful and not just noise? With AI and machine learning automating much of the(Read More)
Data mining is often described as the process of discovering patterns, correlations, and trends within large datasets to generate actionable insights. But in today’s context—where data is abundant and growing exponentially—how do we ensure that the patterns we uncover are truly meaningful and not just noise? With AI and machine learning automating much of the process, what role should human judgment still play in interpreting the results of data mining? Would love to hear how others approach this balance between automation and insight! And are there still people out there who are working as data miners as AI is alreaady here?
Projects PX
Kavya Iyer
Shweta Dubey
PangaeXIn your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?What indicators or monitoring strategies help you decide when updates are needed?
In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?
What indicators or monitoring strategies help you decide when updates are needed?
Projects PX
