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Preparing Financial Data for Machine Learning

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Details

Provider

CodeSignal
Pricing

Free Certificate
Languages

English
Certificate

Certificate Available
Duration & workload

2 hours
Sessions

On-Demand
Level

Intermediate

Found in

Part of

Intro to Machine Learning in Trading with $TSLA

Overview

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.

Syllabus

Unit 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

Unit 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

Unit 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

Unit 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

Unit 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

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