The applications of AI in the financial sector are multi-faceted. In this article, we explore some key use cases of AI for hedge funds. We also look at potential challenges in implementing AI-based solutions, and how hedge funds can circumvent these challenges. In an analysis done in 2020, consulting and…

AI is a sector that is often perceived as having both positive and negative impacts on the environment. On the one hand, AI is able to provide innovative and scalable solutions to tackle the root causes of climate change . Measuring and reducing emissions at scale and enabling innovative business…

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. …

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…

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. …

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…

Five common methods to select the most relevant and useful features, improving the efficiency and effectiveness of model training. — 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. …

In the real-world, dataset collection is loosely controlled, noisy, unreliable, redundant, and incomplete. This makes data pre-processing an integral stage in the machine learning pipeline. In our previous blogs, we introduced how to improve data quality and how to handle imbalanced data. …

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…