Exploratory Data Analysis (EDA) is all about getting to know your data inside and out. It's like going on a first date with your dataset – you want to understand its quirks, strengths, and potential red flags before diving deeper.
EDA involves cleaning up messy data, finding hidden patterns, and visualizing relationships. By using techniques like correlation analysis and dimensionality reduction, you can uncover insights that'll shape your analysis approach and help you build better models.
Data Preprocessing
- Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the dataset to improve data quality
- Includes handling missing values, outliers, duplicates, and inconsistent formatting
- Data transformation converts data from one format or structure to another
- Enables compatibility with analysis tools and techniques
- Common transformations: normalization, standardization, log transformation, and encoding categorical variables
Feature Engineering and Missing Value Analysis
- Feature engineering creates new features or variables from existing data to improve model performance and interpretability
- Combines, extracts, or transforms original features (feature extraction, feature construction)
- Enhances the predictive power of the dataset
- Missing value analysis assesses the extent and patterns of missing data in a dataset
- Determines the impact of missing values on the analysis and modeling process
- Strategies for handling missing values: deletion, imputation (mean, median, mode, regression), or advanced techniques (multiple imputation, k-nearest neighbors)
Exploratory Analysis
Correlation Analysis
- Correlation analysis measures the strength and direction of the relationship between two or more variables
- Pearson correlation coefficient quantifies the linear relationship between continuous variables
- Values range from -1 (perfect negative correlation) to +1 (perfect positive correlation)
- 0 indicates no linear correlation
- Spearman's rank correlation assesses the monotonic relationship between variables, including ordinal data
- Kendall's tau measures the ordinal association between variables
- Correlation matrices visualize pairwise correlations between multiple variables
- Heatmaps or scatterplot matrices (pairs plots) are common visualization techniques
Dimensionality Reduction
Principal Component Analysis (PCA)
- PCA is a linear dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space
- Identifies the principal components, which are orthogonal linear combinations of the original features that capture the maximum variance in the data
- Preserves the essential structure and information while reducing dimensionality
- Useful for visualization, noise reduction, and feature extraction
- Eigenvalues and eigenvectors are key concepts in PCA
- Eigenvalues represent the amount of variance explained by each principal component
- Eigenvectors define the direction of the principal components
Dimensionality Reduction Techniques
- Dimensionality reduction aims to reduce the number of features or variables in a dataset while retaining the most relevant information
- Helps mitigate the curse of dimensionality, improves computational efficiency, and reduces overfitting
- Linear techniques: PCA, Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD)
- Non-linear techniques: t-SNE (t-Distributed Stochastic Neighbor Embedding), UMAP (Uniform Manifold Approximation and Projection), Isomap, Locally Linear Embedding (LLE)
- Feature selection methods identify the most informative features based on various criteria (variance, correlation, information gain, chi-square)