Exploratory Data Analysis

Exploratory Data Analysis (EDA) is the most important first step you'll take after choosing a competition and its dataset. It's a practical, hands-on exercise to understand what you're working with, which will guide the entire direction of your research. This guide will walk you through the key steps.

Getting Started: Loading Data

The first step is always to download the data and load it into a suitable analysis tool. The tool you choose depends on the size of the dataset. For smaller datasets that fit in memory (megabytes to a few gigabytes), libraries like Pandas or Polars are excellent. For larger datasets (tens or hundreds of gigabytes), a distributed computing framework like PySpark is necessary to process the data efficiently.

For this guide, we'll use the classic 20 Newsgroups dataset, which is small enough for Pandas.

import pandas as pd
from sklearn.datasets import fetch_20newsgroups

# Fetch the dataset
newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))

# Convert to a Pandas DataFrame for easier manipulation
df = pd.DataFrame({
    'text': newsgroups.data,
    'target': newsgroups.target
})

# Map target index to the actual category name
df['category'] = df['target'].apply(lambda x: newsgroups.target_names[x])

print(df.head())

Core EDA Tasks

Once the data is loaded, you can begin the analysis. The goal is to move from high-level statistics to a deeper understanding of the data's structure and content.

Basic Statistics and Schema

Start by getting a feel for the dataset's size and shape. You should answer these basic questions:

  • How many rows are there? This gives you the overall scale.
  • What is the schema? What are the column names and their data types?
  • What are the text statistics? Calculate the average number of words, sentences, and tokens per document. Token counts are especially useful for estimating the potential cost of using large language models (LLMs).
# Get the number of rows
num_rows = len(df)
print(f"Number of documents: {num_rows}")

# Get the schema (column names and types)
print("\nSchema:")
print(df.info())

# Get basic text statistics
df['word_count'] = df['text'].apply(lambda x: len(x.split()))
print("\nBasic Text Statistics:")
print(df['word_count'].describe())

Distribution Analysis

Next, look at the distribution of your inputs and outputs.

  • Token Frequency: Tokenize the entire dataset and plot the frequency of each token. In natural language, this distribution almost always follows a Zipfian (log-log) distribution, where a few words are very common and most words are rare.
  • Class Distribution: If you're doing a classification task, plot the number of examples in each category. Real-world datasets are often unbalanced or skewed, with some categories having many more examples than others. Understanding this imbalance is critical for model training and evaluation.
import matplotlib.pyplot as plt
import seaborn as sns

# Plot the distribution of the output classes
plt.figure(figsize=(12, 6))
sns.countplot(y=df['category'], order=df['category'].value_counts().index)
plt.title('Distribution of Categories in 20 Newsgroups Dataset')
plt.xlabel('Number of Documents')
plt.ylabel('Category')
plt.show()

Semantic Analysis and Baseline Modeling

For a deeper analysis, you can explore the semantic content of the data.

  • Topic Modeling: Use a simple topic model like Latent Dirichlet Allocation (LDA) to discover the hidden thematic structures within the text of different categories. This can help you understand if the categories are semantically distinct.
  • Build a Baseline Model: You don't need a complex model for EDA. A simple approach is to embed your text using a pre-trained model and then train a basic classifier, like Logistic Regression, on those embeddings. This gives you a quick performance baseline and helps verify that your data is suitable for the task.

Best Practices for Your EDA Notebook

Your EDA is not a one-off script; it's a foundational document for your project. To make it useful for yourself and your teammates, you should:

  • Be helpful to your future self. Your analysis should be easy to understand weeks or months later.
  • Use titles and markdown headers to structure your notebook into logical sections.
  • Write explanatory text. Don't just show code and plots. Write a few sentences explaining what you're doing and what your findings are. You can even use generative AI to help you phrase your thoughts if writing isn't your strong suit.

Ultimately, the goal of EDA is to explore, ask questions, and build an intuition for the data that will inform every subsequent decision you make in the competition.