Exploratory Data Analysis Case Study - Global Cargo Data

What and Why Exploratory Data Analysis?

Exploratory data analysis(EDA) is used by data analysts/ Scientists to analyze and investigate data and datasets and summarize thier main features, often employing data visualization methods. It helps in understanding the dataset very easily. Helps in manipulating data for further use.

EDA is basically used to see what data can reveal beyond the formal modelling or hypothesis testing task and provides better understanding of data set variables and the relationship between them. Originally developed by American mathematician Jhon Tukey in the 1970s, EDA techniques continue to be a widely used method in the data discovery process today.

EDA can help us deliver great business results, by improving our existing knowledge and can also help in giving out new insights that we might not be aware of

Tools Used

• opendatasets (Jovian library to download a Kaggle dataset)

• Data cleaning:

  1.Pandas

  2.Numpy

• Data Visualization

  1.Matplotlib

  2.Seaborn

  3.plotly

  4.Heatmap