Data Visualization Project

This is a data analytics project utilizing Python v.3 libraries Numpy, PANDAS, Matplotlib and seaborn through Jupyter Notebook in order to analyze data regarding loans from Prosper Marketplace, Inc. The focus of this project is data visualization wherein we undergo two different types: exploratory univariate, bivariate and multivariate visualizations and explanatory visualizations

Required Software:

Jupyter Notebook

Numpy

PANDAS

Matplotlib

Seaborn

Data Analysis Outline:

The first step in data analysis is introducing the data and discussing the objectives. It is also in this step that I outlined the content of the data analysis process.

The second step of data wrangling is assessment wherein we utilize both visual and programmatic assessment methods in order to assess data quality and tidiness issues that we need to address prior to analyzing data.

The third step the data wrangling process is the cleaning stage wherein we use several pandas methods in order to clean any quality and tidiness issues we've detected during the assessment process.

The fourth part is the data visualization process, which is divided into two distinct categories: exploratory and explanatory visualization. The exploratory visualization is where we are searching for relationships or hidden insights to create & answer questions about the data. On the other hand, the explanatory visualization is the one that explains the insights I've found in my data.