Exploring Data for Machine Learning

Imagine embarking on a journey through an expansive ocean of data, where within this vastness are untold stories, patterns, and insights waiting to be discovered. Welcome to the world of data exploration in machine learning (ML). In this chapter, I encourage you to put on your analytical lenses as we embark on a thrilling quest. Here, we will delve deep into the heart of your data, armed with powerful techniques and heuristics, to uncover its secrets. As you embark on this adventure, you will discover that beneath the surface of raw numbers and statistics, there exists a treasure trove of patterns that, once revealed, can transform your data into a valuable asset. The journey begins with exploratory data analysis (EDA), a crucial phase where we unravel the mysteries of data, laying the foundation for automated labeling and, ultimately, building smarter and more accurate ML models. In this age of generative AI, the preparation of quality training data is essential to the fine-tuning of domain-specific large language models (LLMs). Fine-tuning involves the curation of additional domain-specific labeled data for training publicly available LLMs. So, fasten your seatbelts for a captivating voyage into the art and science of data exploration for data labeling.

First, let’s start with the question: What is data exploration? It is the initial phase of data analysis, where raw data is examined, visualized, and summarized to uncover patterns, trends, and insights. It serves as a crucial step in understanding the nature of the data before applying advanced analytics or ML techniques.

In this chapter, we will explore tabular data using various libraries and packages in Python, including Pandas, NumPy, and Seaborn. We will also plot different bar charts and histograms to visualize data to find the relationships between various features, which is useful for labeling data. We will be exploring the Income dataset located in this book’s GitHub repository (a link for which is located in the Technical requirements section). A good understanding of the data is necessary in order to define business rules, identify matching patterns, and, subsequently, label the data using Python labeling functions.

By the end of this chapter, we will be able to generate summary statistics for the given dataset. We will derive aggregates of the features for each target group. We will also learn how to perform univariate and bivariate analyses of the features in the given dataset. We will create a report using the ydata-profiling library.

We’re going to cover the following main topics:

• EDA and data labeling
• Summary statistics and data aggregates with Pandas
• Data visualization with Seaborn for univariate and bivariate analysis
• Profiling data using the ydata-profiling library
• Unlocking insights from data with OpenAI and LangChain