Here you’ll find all articles related to exploratory data analysis (EDA), whether its Python tutorials or conceptual articles.
EDA is used to analyze and examine data sets and summarize their main characteristics. This process often involves the use of data visualization techniques. EDA plays an important role in developing machine learning models in the context of feature engineering, for example, by making relationships between features and target variables apparent. It makes it easier for data scientists to discover patterns, detect anomalies, test a hypothesis, or verify assumptions. It also plays an important role in creating, discovering, and selecting features for machine learning (feature engineering). For example, EDA can highlight relationships among variables that help data scientists to select and improve a feature subset.
The goal of EDA is to uncover the underlying structure and patterns in a dataset, and to identify any potential anomalies or problems that need to be addressed. This can be done using a variety of techniques, such as visualizing the data using plots and graphs, computing summary statistics, or fitting simple models to the data.
EDA is an important step in the data analysis process, because it allows analysts to gain insights and understanding of the data that can inform subsequent steps in the analysis. It is also an iterative process, and analysts may go back and forth between different techniques and approaches as they mixes to other data sets.
Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine learning algorithms, businesses can predict equipment failures before they happen. This approach can help businesses improve their operations by reducing the need for reactive, unplanned maintenance and by enabling them to schedule maintenance activities during … Read more
Have you ever found yourself wondering how you can better understand your customer base and target your marketing efforts more effectively? One solution is to use hierarchical clustering, a method of grouping customers into clusters based on their characteristics and behaviors. By dividing your customers into distinct groups, you can tailor your marketing campaigns and … Read more
Training a machine learning model is like baking a cake: the quality of the end result depends on the ingredients you put in. If your input data is poor, your predictions will be too. But with the right ingredients – in this case, carefully selected input features – you can create a model that’s both … Read more
