##Red Wine Quality
Project overview
Exploratory Data Analysis (EDA) is the numerical and graphical examination of data characteristics and relationships before formal, rigorous statistical analyses are applied.
EDA can lead to insights, which may uncover to other questions, and eventually predictive models. It also is an important “line of defense” against bad data and is an opportunity to notice that your assumptions or intuitions about a data set are violated.
Red Wine Quality project helped me to
- Understand the distribution of a variable and to check for anomalies and outliers
- Learn how to quantify and visualize individual variables within a data set by using appropriate plots such as scatter plots, histograms, bar charts, and box plots
- Explore variables to identify the most important variables and relationships within a data set before building predictive models; calculate correlations, and investigate conditional means
- Learn powerful methods and visualizations for examining relationships among multiple variables, such as reshaping data frames and using aesthetics like color and shape to uncover more information