Exploratory Data Analysis (EDA) is the process of examining and understanding a dataset to uncover its structure, patterns, relationships, and anomalies. It’s a critical first step in any data science or machine learning workflow, helping analysts and data scientists make informed decisions before modeling.
One of the first tasks in EDA is identifying the structure of the data—whether the variables are continuous, ordinal, discrete, or categorical. This classification guides how we analyze and visualize each feature.
When preparing data for model training, it's essential to detect and investigate anomalies or outliers. These unusual data points can distort model performance. Depending on the business context, you may choose to remove them for a more stable model or retain them if they carry meaningful information.
Exploring your data is fundamentally important because it reveals relationships between variables, which can inform feature selection and improve predictive accuracy. For example, identifying which features correlate strongly with the target variable can guide model design.
Visualizations play a key role in EDA. Tools like histograms help assess the distribution of data—whether it's normally distributed or skewed. This insight can influence decisions about data transformations, such as normalization or log-scaling.
EDA helps uncover correlations between input features and the target variable, which is essential for building effective predictive models. One powerful tool for this is a correlation matrix, often visualized as a heatmap.
A heatmap allows us to quickly identify which features have strong positive or negative correlations with the target. These insights guide feature selection, helping us choose the most informative variables for training a machine learning model.
However, correlation matrices only capture linear relationships. This means they can miss important non-linear patterns or suggest misleading associations:
To detect and handle non-linear relationships, consider these techniques:
When estimating the central location of a dataset, the mean is commonly used. However, the mean is highly sensitive to outliers, especially extreme ones, which can distort the true center of the data.
In quantitative finance, analysts use various modeling techniques to understand and predict security prices. One practical application is pairs trading, where the goal is to identify two correlated stocks and exploit temporary divergences in their price relationship.
While raw stock prices alone may not reveal much, plotting relative percentage moves or price ratios between two securities can uncover meaningful patterns. This is where EDA becomes essential—it helps visualize relationships, detect anomalies, and guide strategy development.
Companies operating in the same industry often exhibit similar price behavior due to shared market forces. For example:
EDA can help uncover alike securities using clustering algorithms such as KMeans, which groups stocks based on similar movement patterns. Once clusters are formed, analysts can focus on pairs within the same group.
To detect potential trading signals:
Suppose Pepsi trades significantly lower relative to Coca-Cola, and KDE reveals this price ratio lies in a low-density region—indicating an anomaly. A potential strategy would be to:
The trade profits when the historical relationship converges. This could happen in several ways:
Understanding the distribution of financial data is essential for identifying outliers, assessing risk, and making informed trading decisions. In the context of pairs trading, analyzing the distribution of a price ratio between two securities can help determine whether the current relationship is statistically unusual.
One common technique is to compute the z-score of the price ratio:
z = (x - μ) / σ
A high absolute z-score (e.g., > 2 or < -2) suggests that the ratio is far from its mean, potentially indicating a trading opportunity.
While z-scores assume a normal distribution, financial data often exhibit fat tails and skewness. In such cases, percentile-based analysis can offer a more robust alternative:
By combining z-score and percentile analysis, traders can:
Quantify how far a ratio has deviated from its norm.
Filter out noise and focus on statistically significant signals.
Build mean-reversion strategies that capitalize on the tendency of ratios to revert to historical averages.