Exploratory Data Analysis (EDA) is a process of examining the data to find hidden patterns and trends, find anomalies, and check assumptions using statistical concepts and visualization techniques. By performing EDA, we can gain some useful insights from the data and these insights could be very helpful in the Data Science project. EDA is one of the earliest and most important steps in the Data Science Life Cycle.
I have used Python and its libraries - 1) Pandas and NumPy for data analysis and manipulation, 2) Matplotlib and Seaborn for data visualization.
In this project, I have done EDA on Marketing Campaign Dataset. You can click on the 'EDA on Marketing Campaign Dataset' directory above to know the details about the project and view the project in either PDF format or iPython notebook format. Or you can click here to open: EDA on Marketing Campaign Dataset