Preparing Your Data Set for Machine Learning
In the evolving fields of artificial intelligence (AI) and machine learning (ML), data quality is pivotal to success. Preparing data accurately impacts model performance, making each step of data lifecycle management essential. Cadeon specializes in guiding organizations through this lifecycle with tailored solutions in data governance, data visualization, and advanced analytics. This article explores the key stages in data preparation for machine learning, showing how Cadeon’s expertise can help you transform raw data into actionable insights, empowering business decisions with confidence and precision.
1. Understanding Your Data
Cadeon’s expertise in data governance can empower clients to accurately assess and manage the types of data they’re working with. By implementing best practices in data governance, businesses gain a clear understanding of their data’s origin, structure, and compliance, making it easier to set up ML projects with a solid foundation. This ensures alignment with both business and regulatory requirements, improving data reliability and trustworthiness.
Before diving into data preprocessing, it’s important to have a solid understanding of the dataset you’re working with. Ask yourself:
- What type of data is it (structured, unstructured, or semi-structured)?
- What is the goal of using this data?
- How do you expect the machine learning model to use the data?
This initial analysis helps set the direction for data cleaning, feature engineering, and the types of algorithms that may work best for your task. Consulting with our team at Cadeon, who are experienced visual data scientists, can also provide valuable insights to ensure your data is aligned with your machine learning goals and maximizes its potential for actionable results.
2. Data Collection
Machine learning models are only as good as the data they are trained on, so collecting relevant and high-quality data is essential. Data can come from a variety of sources, including databases, APIs, web scraping, and more. Ensure that you have enough data to support the model’s learning process and that the data is representative of the problem you are trying to solve.
3. Data Cleaning
Data cleaning is one of the most time-consuming yet vital steps in the process. Raw data is often messy, containing missing values, duplicates, or incorrect entries. To clean your data:
- Handle missing values: Fill in missing data using statistical techniques like mean, and median, or by applying more advanced methods such as regression imputation. Alternatively, you may choose to remove rows with missing data if the percentage is low.
- Remove duplicates: Ensure that there are no identical rows or entries, as duplicates can skew your model’s predictions.
- Correct inconsistent data: Sometimes data entries may have formatting errors or typos. Standardizing entries like dates, units of measure, and categorical values is crucial.
4. Feature Selection and Engineering
Once the data set for machine learning is clean, the next step is to identify which features (columns or variables) in your dataset are most relevant for predicting the outcome you’re interested in. This process, called feature selection, helps reduce the complexity of the model and enhances performance.
In addition, you may need to create new features or modify existing ones, a process called feature engineering. For example, if you have a date column, you can extract features like the day of the week, month, or year to add more predictive power.
5. Data Visualization
Before feeding your data into a machine learning model, it’s useful to apply good data visualization techniques to understand your dataset’s structure and relationships between variables. Cadeon’s advanced data visualization solutions are essential for uncovering insights early in the data preparation process. Incorporating Cadeon’s visual data expertise allows clients to make sense of complex datasets, easily identify trends, and visualize relationships that inform better feature selection and model readiness. Cadeon’s visualization tools, combined with techniques like Seaborn and Plotly, offer intuitive, accessible views of critical data insights.
6. Data Transformation
Machine learning algorithms often perform better when the data is standardized or normalized, especially when working with numerical data. Here are common transformations:
- Normalization: Scaling your data so that it falls within a specific range (often between 0 and 1). This is particularly important for algorithms like gradient descent.
- Standardization: This involves subtracting the mean and dividing by the standard deviation so that the data is centred around zero with a standard deviation of one.
Additionally, if your data contains categorical variables, you may need to convert them into a numerical format using techniques like one-hot encoding.
7. Splitting Your Data
To evaluate the performance of your machine learning model, you’ll need to split your data into two main parts: training and testing sets.
- Training set: This is the portion of your data that the model will learn from.
- Testing set: This is used to evaluate the performance of your model on unseen data.
A common split ratio is 80% for training and 20% for testing, although this can vary depending on your dataset size and the complexity of the problem, aligning with best practices in AI data management.
8. Handling Imbalanced Data
Addressing imbalanced data is a key part of data governance. Cadeon’s tailored strategies ensure that data governance practices support fair and unbiased model outcomes. This might involve implementing cost-sensitive learning techniques or leveraging synthetic data generation, both supported by Cadeon’s governance expertise, to create models that remain balanced and representative of real-world scenarios.
For classification problems, you may encounter imbalanced datasets where one class dominates the others (e.g., fraud detection where most transactions are legitimate, and only a small fraction are fraudulent). Imbalanced data can lead to biased models that perform well on the majority class but poorly on the minority class.
There are several ways to handle imbalanced data:
- Resampling: Oversampling the minority class or undersampling the majority class.
- Synthetic data generation: Using techniques like SMOTE (Synthetic Minority Over-sampling Technique) to generate new examples for the minority class.
- Cost-sensitive learning: Assigning a higher penalty for misclassifying examples from the minority class.
9. Data Augmentation
In cases where you don’t have enough data or want to enrich your dataset, data augmentation can be a useful technique. This is particularly common in image and text data, where you can apply transformations like rotations, flips, or noise injection to create new training samples. While augmentation is less common in structured data, similar data strategies can be applied depending on the use case.
10. Dealing with Outliers
Outliers are data points that significantly differ from the rest of your dataset. While they can affect machine learning model performance, they often deserve careful consideration before treatment. Here’s how to approach outliers:
- Remove outliers: If you’ve verified they are errors or irrelevant to the problem.
- Cap values: Set a limit for maximum or minimum values, also known as winsorizing.
- Transform data: Apply logarithmic or square root transformations to reduce the effect of large values.
- Investigate outliers as opportunities: In many cases, particularly in industrial settings, outliers can signal important improvement opportunities or early warning signs. For example, equipment degradation often manifests as intermittent periods of abnormal readings in parameters like noise levels, vibration, temperature, or voltage fluctuations. These outliers could be valuable predictors of impending failures, making them crucial data points for preventive maintenance and process optimization.
Conclusion
Effective data preparation is the foundation of any successful machine learning project. Cadeon’s comprehensive data lifecycle management services ensure your data is not only clean and well-structured but also governed and visualized to drive impactful results. With a unique focus on data governance and visualization, Cadeon’s approach equips you with tools and support for meaningful, high-impact ML models.
Ready to elevate your data strategy?
Book a call with Cadeon to explore how our solutions can empower your machine learning initiatives and turn your data into a strategic asset.
