Preparing Financial Data for Machine Learning

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.

Lessons and practices

Lesson 1: Feature Engineering for ML

Modify Tesla Stock Data Columns

Debug the Tesla Stock Code

Creating and Inspecting New Financial Features

Create New Features for Tesla Stock Data

Tesla Stock Feature Engineering

Lesson 2: Scaling Features with StandardScaler

Scaling a Single Feature with StandardScaler

Identify and Fix the Code

Scaling Financial Features with StandardScaler

Implement Feature Scaling Using StandardScaler

Final Data Scaling Implementation

Lesson 3: Splitting Dataset into Training and Testing Set

Adjust the Dataset Split Ratio

Fix the Dataset Split

Fill in the Blanks: Splitting and Scaling Data

Splitting the Dataset into Training and Testing Sets

Preprocess and Split Tesla Stock Data

Lesson 4: Addressing Data Leakage in Time Series

Adjusting TimeSeriesSplit to 5 Splits

Fixing Time Series Data Split

Ensure Proper Scaling in Time Series Splitting

Feature Scaling and Time Series Split

Addressing Data Leakage in Time Series

Lesson 5: Creating Lag Features for Time Series Prediction

Creating Lag Features for Two Days

Fix the Lag Feature Code

Adding Lag Features and Handling NaN Values

Creating and Using Lag Features for Stock Price Prediction

Interested in this course? Learn and practice with Cosmo!

Practice is how you turn knowledge into actual skills.