A tour of infamous machine learning blunders and failures that caught the world’s attention — The transformative potential of artificial intelligence (AI) and machine learning has often made headlines in the news, with plenty of reports on its positive impact in diverse fields ranging from healthcare to finance. Yet, no technology is immune to missteps. While the success stories paint a picture of machine learning's…

Build a keyword analysis Python application comprising a frontend user interface and backend pipeline — As the amount of textual data from sources like social media, customer reviews, and online platforms grows exponentially, we must be able to make sense of this unstructured data. Keyword extraction and analysis are powerful natural language processing (NLP) techniques that enable us to achieve that.

A simple Excel trick to switch and flip the day and month parts of dates — The Problem Working with time series data in Excel can quickly become a nightmare when the date columns are formatted wrongly. One common problem is that the arrangement of the day, month, and year components is stored initially in a specific format but parsed differently when opened in Excel. For example, dates…

Clearly-explained step-by-step tutorial for implementing transfer learning in image classification — Often we do not have access to a wealth of labeled data or computing power to build image classification deep learning models from scratch. Fortunately, transfer learning empowers us to develop robust image classifiers for our specific classification tasks, even if we have limited resources. In this easy-to-follow walkthrough, we…

Easily access MySQL databases and execute SQL queries in Python — SQL and Python are indispensable tools for data practitioners to work effectively with data. A common use case would be the initial retrieval of data from relational databases using SQL queries, followed by subsequent manipulation and analysis of the data in Python with libraries such as pandas. But did you…

Implementing Amazon's DataWig in Python to impute missing values in tabular data — Missing values in real-world datasets is a common phenomenon that poses a key challenge for all data practitioners. This issue is even more challenging when the dataset contains heterogeneous data types. In this article, we look at how DataWig can help us perform the imputation of missing values in tabular…

Get up to speed with the various data missingness types and methods for imputation — Contents (1) Types of Missing Data (2) Imputation Techniques (3) Python Packages for Imputation (1) Types of Missing Data There are three general types of missing data, best explained with examples. (i) Missing completely at random (MCAR) The likelihood of missing values in a feature is unrelated to any other data features (observed or unobserved). …

Concept and implementation of the global search algorithm to select the best features for machine learning — Feature selection is vital in machine learning as it boosts computational efficiency and predictive performance by keeping only the most relevant predictors. Beyond popular feature selection classes like filter and wrapper methods, global search methods like simulated annealing are powerful techniques at our disposal.

Exploring examples of data science applications across the insurance value chain — The insurance sector is one of the world’s largest industries based on the value of gross premiums, the scale of investments, and its ubiquitous societal role in covering personal and commercial risks. The sheer size of the industry brings a wealth of data and business opportunities, paving the way for…