Introduction

Data visualization is an important part of data analysis and communication because it allows you to visually represent and interpret data in a way that is easily understandable by others. It helps you to identify patterns, trends, and relationships in data that may not be immediately apparent when looking at raw data.

Python is a popular programming language for data analysis and visualization because it offers a wide range of libraries and tools that simplify working with data. For example, the matplotlib library is a powerful tool for creating a variety of charts and plots, while the Seaborn library provides a higher-level interface that makes it easier to generate complex and visually appealing plots.

Using data visualization in Python can help you to effectively communicate your findings to others, whether it be through reports, presentations, or interactive dashboards. It can also help you to identify patterns and trends in data that can inform decision-making or guide further analysis.

There are many different types of graphs that can be used in Python, and the most important ones will depend on the specific data and visualization goals you have. In this project we will explore 6 types of visualization, to be used in different steps of our analysis, what will help us reaching our target. Never forget that the way you deliver your results is as important as the analysis part (if not more).

Overall, data visualization is an essential part of working with data and Python is a great language for creating visualizations. With that being said, let's start with our project!