Machine Learning
513 learners
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.
Pandas
Python
Scikit-learn
5 lessons
24 practices
2 hours
Feature Engineering
Lessons and practices
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
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
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
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
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
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