Who is a data scientist? What does he do? What steps are involved in executing an end-to-end data science project? What roles are available in the industry? Will I need to be a good ...

Have you ever spent hours wrestling with messy spreadsheets, only to end up questioning your sanity over rogue spaces or mismatched text entries? If so, you’re not alone. Data cleaning is one of the ...

Challenges with data quality and data governance have plagued healthcare analytics efforts for decades – and the stakes are only getting higher in the age of AI. Inaccurate or inconsistent data ...

Abstract: The optimization and generalization of performance of a machine learning model is profoundly influenced by efficient data preprocessing. A machine's learning model does not perform to its ...

Data is the foundation of any data-driven initiative, fueling decision-making, insights, and innovation. But, raw data is often inaccurate and inconsistent, which can impede analysis and lead to ...

The Olist Store Analysis project aims to leverage eCommerce data to derive actionable insights that can enhance business strategies, optimize customer experience, and improve overall sales performance ...

In the rapidly evolving AI landscape, companies are racing to deploy the most sophisticated models and cutting-edge algorithms. But amid the excitement, many organizations overlook the most critical ...

Grass-roots initiatives such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data- sharing Initiative (INDI) [1] are successfully amassing and sharing large-scale brain ...

Abstract: To minimize its idle time automated guided vehicle (AGV) is an important task of predicting its battery discharge. This is important for increasing the efficiency of resource use and ...

Imagine this: you’ve just received a dataset for an urgent project. At first glance, it’s a mess—duplicate entries, missing values, inconsistent formats, and columns that don’t make sense. You know ...