detail we will be using python in this bookyou can refer to "the python machine learning ecosystemto understand more about python and the various tools and frameworks used in machine learning you can follow along with the code snippets in this section or open the predicting student recommendation machine learning pipeline ipynb jupyter notebook by running jupyter notebook in the command line/terminal in the same directory as this notebook you can then run the relevant code snippets in the notebook from your browser covers jupyter notebooks in detail objective you have historical student performance data and their grant recommendation outcomes in the form of comma separated value file named student_records csv each data sample consists of the following attributes name (the student nameoverallgrade (overall grade obtainedobedient (whether they were diligent during their course of stayresearchscore (marks obtained in their research workprojectscore (marks obtained in the projectrecommend (whether they got the grant recommendationyou main objective is to build predictive model based on this data such that you can predict for any future student whether they will be recommended for the grant based on their performance attributes data retrieval herewe will leverage the pandas framework to retrieve the data from the csv file the following snippet shows us how to retrieve the data and view it in [ ]import pandas as pd turn of warning messages pd options mode chained_assignment none default='warnget data df pd read_csv('student_records csv'df