Description

EDA in the Data Science Pipeline

• Position in Pipeline: EDA is an essential pre-processing step in the business intelligence (BI) or data science pipeline, occurring after data acquisition but before model training.
• Purpose: The goal of EDA is to understand the data by identifying:
  • Missing values (nulls)
  • Outliers
  • Feature distributions
  • Relationships or correlations between variables

Data Acquisition and Initial Inspection

• Data Sources: Data may arrive from various streams (e.g., Twitter, sensors) and is typically stored in structured formats such as databases or spreadsheets.
• Loading Data: In Python, data is often loaded into a Pandas DataFrame using commands like pd.read_csv('filename.csv').
• Initial Review:
  • df.info(): Displays data types and counts of non-null entries by column, quickly highlighting missing values.
  • df.describe(): Provides summary statistics for each column, including count, mean, standard deviation, min/max, and quartiles.

Handling Missing Data and Outliers

• Imputation:
  • Missing values must often be filled (imputed), as most machine learning algorithms cannot handle nulls.
  • Common strategies: impute with mean, median, or another context-appropriate value.
  • For example, missing ages can be filled with the column's average rather than zero, to avoid introducing skew.
• Outlier Strategy:
  • Outliers can be removed, replaced (e.g., by nulls and subsequently imputed), or left as-is if legitimate.
  • Treatment depends on whether outliers represent true data points or data errors.

Visualization Techniques

• Purpose: Visualizations help reveal data distributions, outliers, and relationships that may not be apparent from raw statistics.
• Common Visualization Tools:
  • Matplotlib: The primary Python library for static data visualizations.
  • Visualization Methods:
    • Histogram: Ideal for visualizing the distribution of a single variable (e.g., age), making outliers visible as isolated bars.
    • Box Plot: Summarizes quartiles, median, and range, with 'whiskers' showing min/max; useful for spotting outliers and understanding data spread.
    • Line Chart: Used for time-series data, highlighting trends and anomalies (e.g., sudden spikes in stock price).
    • Correlation Matrix: Visual grid (often of scatterplots) comparing each feature against every other, helping to detect strong or weak linear relationships between features.

Feature Correlation and Dimensionality

• Correlation Plot:
  • Generated with df.corr() in Pandas to assess linear relationships between features.
  • High correlation between features may suggest redundancy (e.g., number of bedrooms and square footage) and inform feature selection or removal.
• Limitations:
  • While correlation plots provide intuition, automated approaches like Principal Component Analysis (PCA) or autoencoders are typically superior for feature reduction and target prediction tasks.

Data Transformation Prior to Modeling

• Scaling:
  • Machine learning models, especially neural networks, often require input features to be scaled (normalized or standardized).
  • StandardScaler (from scikit-learn): Standardizes features, but is sensitive to outliers.
  • RobustScaler: A variant that compresses the influence of outliers, keeping data within interquartile ranges, simplifying preprocessing steps.

Summary of EDA Workflow

• Initial Steps:
  • Load data into a DataFrame.
  • Examine data types and missing values with df.info().
  • Review summary statistics with df.describe().
• Visualization:
  • Use histograms and box plots to explore feature distributions and detect anomalies.
  • Leverage correlation matrices to identify related features.
• Data Preparation:
  • Impute missing values thoughtfully (e.g., with means or medians).
  • Decide on treatment for outliers: removal, imputation, or scaling with tools like RobustScaler.
• Outcome:
  • Proper EDA ensures that data is cleaned, features are well-understood, and inputs are suitable for effective machine learning model training.