The techniques for data exploration and preparation are typically applied before applying models on the data and this also helps in developing complex statistical models. These techniques are also important for eliminating or sharpening a potential hypothesis which can be addressed by the data. The amount of time spent in preprocessing and data exploration provides the quality input which decides the quality of the output. Once the business hypothesis is ready, a series of steps in data exploration and preparation decides the accuracy of the model and reliable results.
In this chapter, we are going to look at the following common data analysis techniques such as univariate analysis, bivariate analysis, missing values treatment, identifying the outliers, and techniques for variable transformation.
