Data Wrangling & Cleansing, Visualization and Analysis with Python

To-do exercises¶

The longest part of any Data Analysis/science task is preparing and configuring your data properly. A model only performs as well as the data that it is fed and there’s a lot of transformations that the data may have to undergo to be ready for model training. Then we can jump to exploratory data analysis: data visualization, descriptive statistics and modeling.

Note¶

All these examples are in Python and mainly use the Pandas, Numpy, and Sci-Kit Learn libraries. For visualization MatPlotLib or Seaborn was used.