Day #323 - Tech Talk Series - Data Science

Session #1 - Feature Engineering for Tabular Data

Key Notes

• Column Aggregates
• Independent Columns
• Derive New Features
• Target Encoding (Categorical Features)
• Global Feature Encoding (Categorical Features)

Derive Features

• Time - Months, Years, Days, WeekDays, Periods, Distance
• Missing or Not Missing
• Numerical Feature - Scale change, log, exp (Feature Transformations)
• Integer Value, Decimal Value, Mod, Dividend
• Categorical - Merge, One Hot Encoding
• Group Features, Time them, divide them
• Ratio Conversions
• Binning Columns
• Remove Outliers
• Cluster Data and Perform Regression on it

Talk #2 - ML for Optimization Problems

Key Lessons

•  Maximum Something (reward), Minimise something (cost)

Solution Approach

• Linear Optimization
• Linear Objective and set of Linear constraints
• Dynamic Optimization (Reinforcement Learning)
• Non Linear Optimization (Generic Algos, Simulations)

Simulation Optimization

•  Build simulation of real life problem

ML

• Simulate with decision variables

Happy Learning!!!