📈 Exploratory Data Analysis (EDA)

Exploratory Data Analysis is the critical first step in any data science project. It's the process of analyzing datasets to summarize their main characteristics, often using statistical graphics and visualization techniques.

Through EDA, you'll discover patterns, spot anomalies, test hypotheses, and check assumptions before applying machine learning models.

💡 Why is EDA Important?

EDA helps you understand what your data can tell you beyond formal modeling. It prevents "garbage in, garbage out" scenarios and guides feature engineering and model selection decisions.

📰 BBC News Text Classification

Comprehensive EDA tutorial for text classification datasets using BBC News articles. Features interactive visualizations and code examples in Plotly, Matplotlib, and Seaborn.

  • Category distribution analysis
  • Text statistics and distributions
  • Word frequency and TF-IDF analysis
  • N-gram patterns and bigrams
  • Category similarity matrix
  • Interactive tutorials with copy-paste code
🚀 View Interactive Report

💰 Adult Income (Tabular Data)

Comprehensive EDA tutorial for tabular classification datasets using Census Income data. Features interactive visualizations and code examples for structured data analysis.

  • Missing value analysis and imputation
  • Numerical distributions and outliers
  • Categorical feature analysis
  • Correlation matrix and heatmaps
  • Target vs features relationships
  • Interactive tutorials with copy-paste code
🚀 View Interactive Report

🐕 🐈 Oxford Pets (Image Data)

Complete EDA system for image classification using Oxford-IIIT Pet Dataset. Features deep learning-based feature extraction, similarity analysis, and interactive galleries.

  • 37 pet breeds (12 cats + 25 dogs)
  • Image statistics and distributions
  • ResNet50 feature extraction & visualization
  • Breed similarity matrix (37×37)
  • Interactive sample galleries (370 images)
  • Multi-task support (classification, detection, segmentation)
🚀 View Interactive Reports

🛠️ Common EDA Techniques

📊 Univariate Analysis

Analyze single variables to understand their distribution, central tendency, and spread.

🔗 Bivariate Analysis

Examine relationships between two variables using scatter plots and correlation coefficients.

📈 Multivariate Analysis

Understand interactions among multiple variables simultaneously using heatmaps and pair plots.

📉 Missing Data Analysis

Identify patterns in missing values and decide on appropriate imputation strategies.

🎯 Outlier Detection

Find anomalies and extreme values using statistical methods and visualizations.

🔤 Feature Distribution

Analyze how features are distributed using histograms, box plots, and density plots.