Boosting performance is a priority in the AI space. Businesses will not hesitate to make large investments to achieve even a marginal improvement (e.g. increased accuracy, speed) in AI systems. A range of factors impact the performance of AI; high-quality data, infrastructure, code, and the right talent are all crucial elements of a thriving AI […]
[vc_row][vc_column][vc_column_text]Data science is a field that encapsulates a range of skills. However, in the data scientist’s toolbox, coding might become an underrated tool. In order to derive optimal results, a data scientist may choose to focus on a mathematical or conceptual component of their data science task. This strategy might reap greater benefits for tasks […]
In our previous article, Customer Churn Prediction and Prevention Using AI, we pointed out the role of AI in predicting and preventing customer churn. We mentioned that it can cost between 6 to 7 times more to get a new customer, when compared with retaining an existing customer. Since a 5% increase in customer retention […]
Forecasting is a crucial component in decision-making. In order to make good decisions in the present, we must be able to, at least with some accuracy, anticipate future events and the consequences of our actions on those future incidents. Time Series Forecasting Machine learning can be applied to time series data in order to […]
There are several concerns when selecting machine learning models for a specific task. The model performance is of course a prime concern. Besides, the model complexity and speed are vital in scenarios requiring a fast deployment or quick response from models. In this article, we discuss the reasons behind fast and complex models, and how […]
Image by Daniel J. TOTH Hyperparameter tuning plays an important role in the process of training an optimal machine learning model. During the training process, the performance of the target model is evaluated by monitoring metrics such as the values of the loss function or the accuracy score on the test/validation set, on which basis […]
Imbalanced data is a common problem in machine learning, which brings challenges to feature correlation, class separation and evaluation, and results in poor model performance. In this article, we will introduce: Imbalanced data in practice Three challenges with imbalanced data Top 6 data sampling techniques Sampling techniques comparison Python code snippets Imbalanced data in practice […]
Correlations is a measure of the association between variables. They measure to what extent one variable is affected by a change in another variable. In this article, we will explain the importance of data correlation in machine learning, and introduce four common methods to calculate your data correlation. Why Data Correlation is Important? Understanding which […]
In our everyday life we are faced with decisions. One of the reasons why we struggle to take a decision is because, most of the time, it involves more than one objective. For instance, when buying a car, it isn’t just about buying the best car; but about buying a car that you can afford, […]
Data scientists often use Feature Selection techniques to reduce the number of features and keep the most relevant/useful ones before training a ML model on data. It can improve data quality, and help the ML model to focus on the most relevant information in the data, thus improving the efficiency and effectiveness of the training. […]
Introduction With data being at the heart of machine learning, it is inevitable that the performance of all machine learning algorithms is directly affected by the quality of the input data. The saying Garbage in-Garbage out holds in the machine learning case as well: using bad quality data can mislead the training process and result […]