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Discover what features American universities offer that make them the top choice for students. Analyzing data on applications, admissions, tuition fees, and more, this project uses Numpy and Pandas to uncover insights.



The aim of this project is to find out which are the most relevant features that students consider to choose the preferred American university. Some of the essential questions for developing this project are related to the number of applications, admissions, and enrollments, cost of tuition and fees, cost of living on campus, types of degrees offered, and features of the states where universities are located (population and GDP).

The dataset used for this analysis was taken from It contains a plethora of information about American universities (that are not necessarily the top 10-20) in 2013. Although this dataset does not contain information about all the first-ranked American universities, the patterns and insights extracted from it are highly representative of the whole behavior. The dataset contains more than a thousand rows (universities) and 145 columns (features about those universities). Several of those features are out of the scope of this project. Only the features that have information to answer the questions to achieve the goal of the project were deployed. The most powerful tools for data analysis used in this project are the packages Numpy and Pandas, and to visualize and explore the data: Matplotlib and Seaborn. All of these tools were meaningfully and efficiently taught in the course "Data Analysis with Python: Zero to Pandas" given by Jovian in partnership with freeCodeCamp.

As a first step, I'll upload this Jupyter notebook to

file_name ='student_perfere_a_university'
!pip install jovian --upgrade -q