Kishan Manani
Kishan is a machine learning and data science lead, course instructor, and open source software contributor. He contributes to well known Python packages including Statsmodels and Feature-engine. He has 10+ years of experience in applying machine learning and statistics in finance, e-commerce, and healthcare research. He leads data science teams to deliver data and machine learning products end-to-end.
Kishan attained a PhD in Physics from Imperial College London in applied large scale time-series analysis and modelling of cardiac arrhythmias; during this time he taught and supervised undergraduates and master's students.
Twitter: https://twitter.com/KishManani
LinkedIn: https://www.linkedin.com/in/kishanmanani/
Medium: https://medium.com/@kish.manani
Website: https://www.courses.trainindata.com/p/feature-engineering-for-forecasting
Sessions
To use our favourite supervised learning models for time series forecasting we first have to convert time series data into a tabular dataset of features and a target variable. In this talk we’ll discuss all the tips, tricks, and pitfalls in transforming time series data into tabular data for forecasting.