Data Preparation Techniques for Machine Learning

In machine learning, the algorithm learns from the data you feed it. Without well-prepared data, even the most advanced algorithms can produce inaccurate or misleading results. Preparing data is a continuous process rather than a one-time task.

Introduction

Data preparation, also referred to as data preprocessing, is the process of making raw data ready for after processing and analysis to ensure its quality, consistency, and relevance. It lays the foundation for successful analysis and decision-making. This involves collecting, cleaning, and labeling raw data in a format suitable for machine learning (ML) algorithms, followed by data exploration and visualization. Careful data preparation is essential to the success of data analytics.

Why Data Preparation Is Crucial

Several reasons highlight the importance of data preparation in machine learning:

Improves Data Quality

Raw data often contains inconsistencies, missing values, errors, and irrelevant information. Data preparation techniques like cleaning, imputation, and normalization address these issues, resulting in a cleaner and more consistent dataset. This, in turn, prevents these issues from biasing or hindering the learning process of your models.

Enhances Model Performance

Machine learning algorithms rely heavily on the quality of the data they are trained on. By preparing your data effectively, you provide the algorithms with a clear and well-structured foundation for learning patterns and relationships. This leads to models that are better able to generalize and make accurate predictions on unseen data. Poorly prepared data can also lead to overfitting, where the model performs well on the training data but poorly on new, unseen data.

Read also: Data Theory at UCLA

Saves Time and Resources

Investing time upfront in data preparation can significantly save time and resources down the line. By addressing data quality issues early on, you avoid encountering problems later in the modeling process that might require re-work or troubleshooting. This translates to a more efficient and streamlined machine learning workflow.

Facilitates Feature Engineering

Data preparation often involves feature engineering, which is the process of creating new features from existing ones. These new features can be more informative and relevant to the task at hand, ultimately improving the model's ability to learn and make predictions.

The Data Preparation Process

There are a few important steps in the data preparation process, and each one is essential to making sure the data is prepared for analysis or other processing.

Step 1: Describe Purpose and Requirements

Identifying the goals and requirements for the data analysis project is the first step in the data preparation process. What is the goal of the data analysis project and how big is it? Which major inquiries or ideas are you planning to investigate or evaluate using the data? Who are the target audience and end-users for the data analysis findings? What positions and duties do they have? Which formats, types, and sources of data do you need to access and analyze? What requirements do you have for the data in terms of quality, accuracy, completeness, timeliness, and relevance? What are the limitations and ethical, legal, and regulatory issues that you must take into account? With answers to these questions, data analysis project's goals, parameters, and requirements simpler as well as highlighting any challenges, risks, or opportunities that can develop.

Step 2: Data Gathering

Data collection or gathering sounds like a piece of cake. The sources of this data can vary widely depending on your project's requirements. It's important to ensure the data you collect is relevant to the problem you're trying to solve. In most organizations, data is segregated into many departments and even tracking points within departments. If you have various channels of engagement, acquisition, and retention, consolidating all data streams into centralized storage will be challenging. Suitable resources and methods are used to obtain and analyze data from a variety of sources, including files, databases, APIs, and web scraping.

Many organizations start by storing data in warehouses. These are often designed for structured (or SQL) records compatible with conventional table formats. Another common aspect of working with warehouses is transforming data before loading it into the warehouse. This method is known as Extract, Transform, and Load (ETL). The trouble with this strategy is that you never know which data will be valuable to the ML project.

Data lakes are storage systems that can store both structured and unstructured data, such as images, videos, voice recordings, PDF files, etc. You load the data in its current state and determine how to utilize and transform it later, on demand. This method is known as Extract, Load, and Transform (ELT).

Step 3: Data Combining and Integrating

Data integration requires combining data from multiple sources or dimensions in order to create a full, logical dataset. To properly combine and integrate data, it is essential to store and arrange information in a common standard format, such as CSV, JSON, or XML, for easy access and uniform comprehension. Audits, backups, recovery, verification, and encryption are all examples of strong security procedures that can be used to make sure reliable data management.

Step 4: Data Profiling

Data profiling is a systematic method for assessing and analyzing a dataset, making sure its quality, structure, content, and improving accuracy within an organizational context. Data profiling identifies data consistency, differences, and null values by analyzing source data, looking for errors, inconsistencies, and errors, and understanding file structure, content, and relationships.

Step 5: Data Exploring

Data exploration is getting familiar with data, identifying patterns, trends, outliers, and errors in order to better understand it and evaluate the possibilities for analysis. To evaluate data, identify data types, formats, and structures, and calculate descriptive statistics such as mean, median, mode, and variance for each numerical variable.

Common Data Preparation Techniques

Once you've collected your data, the next step is to clean it. The following are some common techniques used in data preparation:

Data Cleaning

Raw data may or may not contain errors and inconsistencies. Hence, drawing actionable insights is not straightforward. We have to prepare the data to rescue us from the pitfalls of incomplete, inaccurate, and unstructured data. Cleaning procedures are used to remove or correct flaws or inconsistencies in your data, such as duplicates, outliers, missing numbers, typos, and formatting difficulties. Analyze the data to better understand its properties, such as data kinds, ranges, and distributions. Identify any potential issues, such as missing values, exceptions, or errors.

Handling Missing Values

Missing values happen when certain numerical values are blank in your dataset. Few datasets are complete. You must decide how to address gaps. For numeric features, use the mean or median depending on the distribution. For categorical features, fill missing entries with the most frequent class. In advanced cases, apply model-based imputation or domain-specific logic. There are several methods and tools available to assist in this task. One commonly used technique is imputation, which involves filling in missing values using statistical approaches such as mean, median, or regression-based imputation.

Imputation is one such method where you replace missing values with estimated ones. For example, suppose you're working with time-series data where continuity and sequence are important. In some cases, particularly when the missing data could introduce bias, it might be better to delete those rows. Many data engineers make missing values a priority since they can significantly affect prediction accuracy. You can automate the process of data cleaning if you use an ML-as-a-Service platform.

Handling Outliers

Outliers are data points that are significantly different from the rest of the data. Outliers can distort training, especially for models sensitive to scale, such as linear regression or k-nearest neighbors. Identify outliers using statistical methods like z-scores or the interquartile range. Visualization methods such as boxplots also help. Once detected, decide whether to remove, cap, or transform them.

One technique to identify outliers is z-score normalization. Z-score normalization is a statistical method that calculates how many standard deviations a data point is from the mean of the dataset. In simpler terms, it helps you understand how "abnormal" a particular data point is compared to the average. Once you've identified outliers using z-score normalization, you have a few options. You can remove them to prevent them from skewing your model or cap them at a certain value to reduce their impact. Data validation techniques can also be helpful here.

Data Transformation

Data transformation is the process of converting your cleaned data into a format suitable for machine learning algorithms. As you work on your problem, you will almost certainly have to review various transformations of your preprocessed data.

Feature Scaling

In marketing data, you might have variables on different scales, like customer age and monthly spending. Feature scaling helps to normalize these variables so that one doesn't disproportionately influence the model. Algorithms that rely on distance or gradient descent require scaled features. Without scaling, features with larger numeric ranges dominate learning.

Two common methods exist:

Standardization: Shift features to zero mean and unit variance.
Normalization: Scale features to a range between 0 and 1.

Choose scaling based on the algorithm. For example, support vector machines and k-means clustering work better with normalization. One popular transformation technique is normalization, which scales numerical data to a standard range, often between 0 and 1, to ensure all variables contribute equally to the analysis. Standardization is another transformation method that adjusts data to have zero mean and unit variance. Both techniques are frequently employed in machine learning algorithms.

Encoding Categorical Variables

Categorical values, such as customer segments or product categories, must be converted to numerical format. For categorical variables, use the right encoding technique. Apply label encoding to ordered categories such as education level. Apply one-hot encoding to unordered categories such as product type. There are two common methods for this: one-hot encoding and label encoding. One-hot encoding creates binary vectors, where each element represents the presence or absence of a specific category.

Decomposition

A date may include day and time components that may be further subdivided for ML purposes. Perhaps just the time of day is important to your project?

Aggregation

Some features can be aggregated into a single feature that is more relevant to your application.

Data Reduction

Data reduction is the process of simplifying your data without losing its essence. The problem is, the more dimensions this space has (e.g. the more input variables), the more likely it is that the dataset represents a very sparse and likely unrepresentative sampling of that space. This is referred to generally as dimensionality reduction and provides an alternative to feature selection. The main impact of these techniques is that they remove linear dependencies between input variables.

Data Splitting

Do not train on all the data. You must set aside test data to evaluate model performance. A common practice is using a 70-30 or 80-20 ratio for training and test sets. The training set is used to train the model, and the test set is used to evaluate it. The most common splits are 80:20 or 70:30 between training and test sets. In cases where hyperparameter tuning is important, add a validation set. Another option is k-fold cross-validation, which trains on multiple folds to reduce bias in evaluation. It's essential to ensure each set represents the overall data.

Additional Data Preparation Steps

Feature Engineering

Once you clean the raw data, prepare it for learning. Split the dataset into features (X) and labels (y). Feature engineering is the process of creating new features or modifying existing ones to enhance the predictive power of machine learning models. Feature engineering requires a combination of domain knowledge, creativity, and statistical techniques.

Data Integration

Data integration involves combining data from multiple sources, which often come in different formats or structures. This process is essential for creating a comprehensive and unified dataset for analysis. Various tools and strategies can aid in data integration.

Data Sampling

Data sampling is a technique used to select a subset of data from a larger dataset for analysis. This method is especially useful when dealing with large datasets or imbalanced classes. Random sampling, stratified sampling, and oversampling are some common sampling strategies.

Data Governance

The first thing you should consider is whether your data can be trusted for ML training. Data quality effort starts at the first stage of data collection, through ingestion and transformation. Data governance is a critical component of data management within a business. In the context of machine learning, the first point is crucial for getting trustworthy and relevant results. Access to sensitive data must be protected, and teams need to follow data protection standards such as GDPR and CCPA. Another area related to governance initiatives centers on tracking the origin and transformations of data as it passes through the ML pipeline. This helps you evaluate the impact of data on model performance and ensure data pipeline traceability. Keeping track of several versions of data can be equally difficult.

Data Version Control

Almost every business is subject to data protection requirements such as GDPR, which require them to maintain specific information in order to verify compliance and the history of data sources. Data version control is essential to automate data quality checks and implement and assert data governance.

Tools for Data Preparation

The following section outlines various tools available for data preparation, essential for addressing quality, consistency, and usability challenges in datasets.

Pandas: Pandas is a powerful Python library for data manipulation and analysis. It provides data structures like DataFrames for efficient data handling and manipulation. Pandas is widely used for cleaning, transforming, and exploring data in Python.
Trifacta Wrangler: Trifacta Wrangler is a data preparation tool that offers a visual and interactive interface for cleaning and structuring data. It supports various data formats and can handle large datasets.
KNIME: KNIME (Konstanz Information Miner) is an open-source platform for data analytics, reporting, and integration. It provides a visual interface for designing data workflows and includes a variety of pre-built nodes for data preparation tasks.
DataWrangler by Stanford: DataWrangler is a web-based tool developed by Stanford that allows users to explore, clean, and transform data through a series of interactive steps. It generates transformation scripts that can be applied to the original data.
RapidMiner: RapidMiner is a data science platform that includes tools for data preparation, machine learning, and model deployment. It offers a visual workflow designer for creating and executing data preparation processes.
Apache Spark: Apache Spark is a distributed computing framework that includes libraries for data processing, including Spark SQL and Spark DataFrame. It is particularly useful for large-scale data preparation tasks.
Microsoft Excel: Excel is a widely used spreadsheet software that includes a variety of data manipulation functions.
Data Cleaning Tools: A data cleaning tool accelerates and streamlines the process by automating numerous operations.
Data Quality Tools: Data quality tools open the door to streamlining and, in many cases, automation of data management tasks that help you ensure that data is good to go for analytics, data science, and machine learning use cases. Using a data quality tool boosts customer confidence in the data.

Automated tools like Pecan can streamline this process, making it easier to turn raw data into actionable information.

Myths About Data Preparation

There are myths about data preparation that can set back your machine-learning efforts. The reality, however, is quite different: quality over quantity. The reason this misconception is so harmful is because it leads to wasted resources. Believing that data preparation is a one-off task can lead to outdated data, affecting your machine-learning model's accuracy in the long term. And it's true that manual oversight can catch nuances automated tools might miss. However, relying only on manual methods has its drawbacks.

Data Prep Checklist

Hereâs a checklist of data prep steps you should check off before building machine learning models.

Scoping a Project

Before diving into modeling, itâs important to take a step back and think about why youâre modeling in the first place.

Step 1. Think like an end user
Step 2. Brainstorm problems & solutions
Step 3. Solidify the ML techniques & data requirements
Step 4. Summarize the scope & objectives

You want to start by thinking of your end user, or the person / team thatâs going to benefit from your analysis. What are their goals? Then together with your end user, youâll want to dig into the problems that theyâre having and brainstorm solutions. At this point, you may discover that while machine learning can be one solution to the problem, there might actually be a better alternative and machine learning is not needed at all! If you do decide to proceed with ML, then itâs important to solidify all the technical details at this point â are you going to use supervised or unsupervised learning techniques, where are you going to get the data, etc. Finally, youâll want to summarize your project goal and scope into a few sentences to be your guiding star throughout your project.

Gathering Data

Now itâs time to get our hands on some data!

Step 5. Locate data from multiple sources
Step 6. Read data into Pandas DataFrames
Step 7. Quickly explore the DataFrames

Once youâve identified your data sources and scope, the next step is to actually find that data, read it into Python as Pandas DataFrames and quickly explore the data using methods like .describe() and .info() to make sure the data was read in correctly. Keep in mind that you donât have to gather all your data upfront. You can always start with one or two data sources, and continue to include more data as youâre cleaning and modeling.

Cleaning Data

This is where data scientists spend the majority of their time.

Step 8. Convert data to the correct data types
Step 9. Identify and handle missing data
Step 10. Identify and handle inconsistent text & typos
Step 11. Identify and handle duplicate data
Step 12. Identify and handle outliers
Step 13. Create new fields from existing fields

One of the first things I like to do when reading in new data is to review the data types of the fields. If anything seems unusual (i.e. a numeric field was read in as a text field), itâs nice to resolve it upfront before running into issues down the line. Next, I go into the main part of data cleaning â dealing with messy data issues. Iâve included four of the most common issues that Iâve seen in practice â missing data, inconsistent data, duplicate data and outliers. Itâs important to resolve these issues before modeling because a model is only as good as its data. Finally, before moving on to exploratory analysis, I like to create new fields based on existing fields, so extracting years and months from date fields, combining fields with a calculation or concatenation, etc.

Exploratory Data Analysis

Once the data is mostly clean (remember that itâs rare to have perfectly clean data!), this is where the fun begins.

Step 14. View the data from multiple angles
Step 15. Visualize the data to quickly identify trends & patterns

With Exploratory Data Analysis (EDA), you can start to discover insights by viewing your data in different ways by filtering, sorting and grouping your data, and also by visualizing your data with histograms, scatter plots and pair plots. Visualizations are useful for both finding patterns that can be shared as insights and also finding anomalies that can lead to further data cleaning.

Preparing for Modeling

The final step before modeling is to get your data into a very specific format that you can input into a machine learning model.

Step 16. Create a single table
Step 17. Set the correct row granularity
Step 18. Ensure each column is non-null and numeric
Step 19. Engineer new features
Step 20. Split the data into training, validation & test sets

First, youâll want to create a single table that holds all of your data, including both the features and the target variable. From there, you need to determine what one row of data should look like. If youâre making predictions about a customer, then one row of data should represent a customer instead of each one of their purchases. This is where a .groupby() comes in handy! Once you have the correct row granularity, youâll need to make sure that your data is non-null and numeric, by potentially imputing data, creating dummy variables, etc.

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