Essential guide to scale-up your Pandas workflow — Pandas is one of the most popular Python libraries for data exploration and visualization. Pandas offer tons of APIs to perform data-wrangling tasks, but when working with large datasets, it fails or has slow computations. In this article, we will discuss 14 techniques to speed up the Pandas workflow using…

Essential guide to the diagnosis prediction in healthcare via attention-based Bi-LSTM network — Predicting future health information or disease using the Electronic Health Record (EHR) is a key use case for research in the healthcare domain. EHR data consists of diagnosis codes, pharmacy codes, and procedure codes. Modeling and Interpreting the model on EHR data is a tedious task due to the high…

Essential guide to time series feature engineering and forecasting — Time series forecasting involves model building on historical time-stamped data values and external factors to make scientific predictions that drive future strategic decision-making. Training a robust time-series forecasting model for accurate and reliable predictions is one of the most challenging tasks, given its direct impact on related decisions. The robustness…

Essential guide to an open-source Python framework for automated feature engineering — Feature engineering is an important and time-consuming component of the data science model development pipeline. The feature engineering pipeline decides the robustness and performance of the model. There are various automated feature engineering packages that process and create features for a single dataset. But these packages fail for the use-cases…

An open-source package for time series preprocessing and forecasting with unified and user-friendly APIs — Time series forecasting involves model building on historical time-stamped data to make scientific predictions and drive future strategic decision-making. Time series forecasting has many uses in various domains including: Predict consumer demand for every product Forecasting pandemic spread, diagnosis, medication, and planning in healthcare

Extract hundreds of relevant features for your time series use-case — Time Series data capture the variable's value repeatedly over time resulting in a series of data points indexed in time order. In time series data has natural temporal ordering i.e. the value of a variable at a particular time is dependent on past values. Traditional machine learning algorithms are not…

Implementation of Exploratory Data Analysis libraries with a few lines of Python code — Exploratory Data Analysis is one of the popular components of a data science model development pipeline. A data scientist spends most of their time performing EDA to generate insights about the data. Automated EDA packages can perform EDA in a few lines of Python code. In this article, we will…

General pitfalls of interpretable machine learning — Interpretable machine learning techniques are becoming more popular among the data science community as more and more complex machine learning algorithms are adopted which are not easily interpretable. Model-Agnostic Interpretation techniques do not care about the underlying models, but they have the capability to interpret the model and provide insightful…

Essential guide to LSAN: Modeling Long-term dependencies and Short-term correlations with Hierarchical Attention — Electronic Health Records (EHR) are comprehensive historical health records that contain the symptoms of a patient when he/she visits a doctor. EHR data has a two-level hierarchical structure that consists of a set of time-ordered visits, and within each visit, there is a set of unordered diagnosis codes. …