This course explores the essential steps for preparing data for machine learning, focusing specifically on financial time series data. From feature engineering to scaling and train-test splitting, you will learn to apply best practices in preprocessing data to pave the way for successful model training and evaluation.
Overview
Syllabus
- Lesson 1: Feature Engineering for ML
- Lesson 2: Scaling Features with StandardScaler
- Lesson 3: Splitting Dataset into Training and Testing Set
- Lesson 4: Addressing Data Leakage in Time Series
- Lesson 5: Creating Lag Features for Time Series Prediction
