See Part I for the basics in data preprocessing and Part II for ready-to-download Python scripts.
Data cleaning vs data preprocessing
In the context of trading, data cleaning may involve handling errors in historical stock prices or addressing inconsistencies in trading volumes.
However, data preprocessing is then applied to prepare the data for technical analysis or machine learning models, including tasks such as scaling prices or encoding categorical variables like stock symbols.
| Aspect | Data Cleaning | Data Preprocessing |
| Objective | Identify and rectify errors or inaccuracies in stock prices. | Transform and enhance raw stock market data for analysis. |
| Focus | Eliminating inconsistencies and errors in historical price data. | Addressing missing values in daily trading volumes and handling outliers. |
| Tasks | Removing duplicate entries. | Scaling stock prices for analysis. |
| Importance | Essential for ensuring accurate historical price data. | Necessary for preparing data for technical analysis and modelling. |
| Example Tasks | Removing days with missing closing prices. Correcting anomalies in historical data. | Scaling stock prices for comparability. Encoding stock symbols. |
| Dependencies | Often performed before technical analysis. | Typically follows data cleaning in the trading data workflow. |
| Outcome | A cleaned dataset with accurate historical stock prices. | A preprocessed dataset ready for technical analysis or algorithmic trading. |
Data preparation vs data preprocessing
Now, let us see how preparing the data is different from data preprocessing with the table below.
| Aspect | Data Preparation | Data Preprocessing |
| Objective | Prepare raw data for analysis or modelling. | Transform and enhance data for improved analysis or modelling. |
| Example Tasks | Collecting data from various sources, combining data from multiple datasets, aggregating data at different levels, and splitting data into training and testing sets. | Imputing missing values in a specific column, scaling numerical features for machine learning models, and encoding categorical variables for analysis. |
| Scope | Broader term encompassing various activities. | A subset of data preparation, focusing on specific transformations. |
| Tasks | Data collection, data cleaning, data integration, data transformation, data reduction and data splitting. | Handling missing data, scaling features, encoding categorical variables, handling outliers, and feature engineering. |
| Importance | Essential for ensuring data availability and organisation. | Necessary for preparing data to improve analysis or model performance. |
| Dependencies | Often precedes data preprocessing in the overall data workflow. | Follows data collection and is closely related to data cleaning. |
| Outcome | Well-organised dataset ready for analysis or modelling. | Preprocessed dataset optimised for specific analytical or modelling tasks. |
This version provides a cleaner presentation of the information without redundancies and unnecessary symbols.
Data preprocessing vs feature engineering
Data preprocessing involves tasks such as handling missing data and scaling, while feature engineering focuses on creating new features or modifying existing ones to improve the predictive power of machine learning models.
Both are crucial steps in the data preparation process. Let us see a table with a clear distinction between the two.
| Aspect | Data Preprocessing | Feature Engineering |
| Objective | Transform and enhance raw data for analysis or modelling. | Create new features or modify existing ones for improved model performance. |
| Example Tasks | Imputing missing values and scaling numerical features. | Creating a feature for the ratio of two existing features and adding polynomial features. |
| Scope | Subset of data preparation, focusing on data transformations. | Specialised tasks within data preparation, focusing on feature creation or modification. |
| Tasks | Handling missing data, scaling and normalisation, encoding categorical variables, handling outliers, and data imputation. Data preprocessing is a broader term which includes the tasks of data cleaning and data preparation as well. | Creating new features based on existing ones, Polynomial features, Interaction terms, and Dimensionality reduction. |
| Importance | Necessary for preparing data for analysis or modelling. | Enhances predictive power by introducing relevant features. |
| Dependencies | Typically follows data cleaning and precedes model training. | Often follows data preprocessing and precedes model training. |
| Outcome | A preprocessed dataset ready for analysis or modelling. | A dataset with engineered features optimised for model performance. |
Where can you learn more about data preprocessing?
Learn more about data preprocessing with our courses mentioned below.
FREE Course | Introduction to Machine Learning in Trading
This course can help you learn the machine learning models and algorithms that are used for trading with the financial market data. Learning about machine learning in detail will help you understand how data preprocessing is essential.
Course | Data & Feature Engineering for Trading
With this course, you will equip yourself with the essential knowledge required for the two most important steps for any machine learning model, which are:
- Data cleaning – This implies making the raw data error free by taking care of issues such as missed values, redundant values, duplicate values etc.
- Feature engineering – To extract the important features for the machine learning model to learn the patterns of the dataset with solutions to similar inputs in future.
Conclusion
Data preprocessing is the prerequisite for making the machine learning model able to read the dataset and learn from the same. Any machine learning model can learn only when the data consists of no redundancy, no noise (outliers), and only such numerical values.
Hence, we discussed how to make the machine learning model learn with data it understands the best, learns from and performs with every time.
Moreover, since understanding the concept of data preprocessing is foundational to both trading and machine learning, we recognize the need for mentioning data preprocessing as a vital step in trading. We delved into the reasons behind its importance and its direct impact on enhancing model performance.
Moving beyond theory, our focus in the blog extended to the practical realm. By exploring real-world examples and hands-on exercises in Python, we covered how to gain proficiency in applying data preprocessing techniques.
These skills are essential for handling various types of datasets effectively which is a key aspect in the intersection of trading and machine learning. Following a systematic set of steps, we went through the steps for preprocessing the data efficiently, that ensure its readiness for machine learning applications.
If you wish to explore more about data preprocessing in detail, explore this comprehensive Feature Engineering course by Quantra where you will find out the importance of data preprocessing in feature engineering while working with machine learning models.
You can master the concepts such as Exploratory Data Analysis, Data Cleaning, Feature Engineering, Technical Indicators, and more. You will get to elevate your skills in creating predictive models and learn the art of backtesting and paper trading. Don’t miss this opportunity to transform your understanding and application of feature engineering. Happy learning!
Author: Chainika Thakar
Originally posted on QuantInsti blog.
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