Feature Engineering & Data Preparation for Modeling — Data Science Ro…
Preparing data well enough that a model has a real chance of learning something useful
Steps in Feature Engineering & Data Preparation for Modeling
- Feature Engineering Fundamentals — advanced · Creating new, more predictive variables from raw data
- Encoding & Scaling for Models — advanced · Preparing categorical and numeric features for algorithms that need them in specific forms
- Handling Imbalanced Data — advanced · Techniques for when the outcome you care about is rare
- Time-Based Feature Engineering — advanced · Extracting lag features, rolling averages and seasonality from time series data
- Basic Text Feature Engineering — advanced · Turning raw text into simple numeric features like word counts and TF-IDF
Part of
- Data Science roadmap — the full learning path