Description:

In Data Science, Garbage In = Garbage Out. Feature engineering is one of most of the important yet most neglected step in life cycle of Machine learning projects. Kaggle competitions have showed us that top Kagglers spend more than half of their time in feature engineering. Through various experiments, its also proved again & again that better features with simple model triumphs even advance models.

In this talk I am planning to discuss basic as well advance feature engineering techniques which can be used by everyone in their projects

Outline

• What is Feature Engineering ?
• Techniques for Numerical Variables
• Techniques for Categorical Variables
• Techniques for Textual data
• Advance techniques
• Feature Selection & Dimensionality reduction
• QA

Prerequisites:

Basic knowledge of Python & Machine learning.

Speaker Info:

• Sudarshan Gadhave is a Data Science ,Data Engineering & Data Integration professional with over 8 years of experience working on Machine Learning , Data Engineering , Data Visualization and Data Warehousing Projects.
  • Currently he is working as a Specialist Data Scientist in Analytics R&D team of Nice Actimize ( Nice Systems) working on developing Anomaly & Fraud detection models.
  • Earlier experience of working in Advance Analytics & Data Warehousing teams of NEC, Japan & John Deere (Deere & Company).
  • Pythonista & expert in Python Machine learning stack (Numpy,Pandas, Scikit-Learn, Matplotlib)
  • Active & Core member of Python Pune meetup group.Presented several talks on Python & machine learning in meetups, conferences and colleges all over Pune.

Speaker Links:

• Github:- https://github.com/sudarshan1413
  • Linkedin:- https://www.linkedin.com/in/sudarshan-gadhave-73567b23/