“In statistics, exploratory data analysis (EDA) is an approach of analyzing datasets to summarize their main characteristics, often with visual methods.” — Wikipedia
Graphical techniques
There are a number of tools that are useful for EDA, but EDA is characterized more by the attitude taken than by particular techniques. Typical graphical techniques used in EDA are:
- Box plot
- Histogram
- Multi-vari chart
- Run chart
- Pareto chart
- Scatter plot
- Stem-and-leaf plot
- Parallel coordinates
- Odds ratio
- Targeted projection pursuit
- Glyph-based visualization methods such as PhenoPlot and Chernoff faces
- Projection methods such as grand tour, guided tour and manual tour
- Interactive versions of these plots
Dimensionality reduction:
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable.
- Multidimensional scaling
- Principal component analysis (PCA)
- Multilinear PCA
- Nonlinear dimensionality reduction (NLDR)
Typical quantitative techniques are:
In Data Analysis, we will analyze to find out the following:
- Dataset’s shape and overview
- Missing values
- All numerical variables
- Distribution of the numerical variables
- Outliers
- Categorical variables
- Cardinality of categorical variables
- Relationship between independent and dependent feature (We will plot and check distributions in each section).
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