Student Performance Prediction Using AI ML

1)  Miss. Anjali Kailas Mali                                                                                                    

              2)  Miss. Prajakta Sadashiv Patil 

              3)  Miss. Vishakha Kailas Patil                                                                                     

               4)  Miss. Kanchan Rahul Suralkar

An education system is one of the most important parts for the development  of  any  country. For a productive and a good life, education is a necessity and it improves individuals' life with value and excellence. Also, education is considered a vital need for motivating self-assurance as well as providing the things needed to partake in today's World. 

Throughout the years, education faced a number of challenges. Different methods of teaching and learning are suggested to increase the learning quality. Predicting academic performance is an important task for the students in university, college, and school, etc In today's world, computers and portable devices are employed in every phase of daily life and many materials are available online anytime, anywhere. Technologies like Machine Learning  & Artificial Intelligence had a surprising evolution in many fields especially in educational teaching and learning processes. 

Higher education institutions have started to adopt the use of technology into their traditional In this paper, we used a machine learning algorithm and descriptive datasets include school, Sex, Age, Address, Famsize, Pstatus, Medu, Fedu, Mjob, Fjob, Reason,Gaurdian,Traveltime,Studytime, Failuress,Schoolsup,Famsup,Paid,Activities,Nursery,Higher,Interne,Romantic,Farmel, Freetime,Goout, Dac, Walc, Health, Absence. have been considered for the prediction and classification of student performance respectively using three machine learning algorithms including Random forest, Decision Tree, Linear Regression  are implemented to predict the student’s academic performance. 

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Miss. Anjali Kailas Mali, the writer of this paper which published on DelvingOrbit.

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