Leafer's Data Exploration
Leafer's Data Exploration uses R, and RStudio to apply exploratory data analysis techniques to explore relationships in one variable to multiple variables and analyzes a selected data set for distributions, outliers, and anomalies. Created by Marie Leaf.
"When information becomes cheap, attention becomes expensive."
Table of contents
Explores Prosper Loan data. This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, borrower employment status, borrower credit history, and the latest payment information.
- Data exploration to identify most important variables and relationships for building predictive models
- Checking anomalies and outliers in variable distributions
- Quantify and visualize individual variables with various univariate, bivariate, multivariate charts and analysis on the data (scatter, histograms, bar, box)
- Calculating correlations, and conditional means
- Reshaping and aesthetic manipulation of dataframes