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Student Marks and performance prediction using machine learning #960
This dataset includes columns like :
Hours Studied: The total number of hours spent studying by each student.
Previous Scores: The scores obtained by students in previous tests.
Extracurricular Activities: Whether the student participates in extracurricular activities (Yes or No).
Sleep Hours: The average number of hours of sleep the student had per day.
Sample Question Papers Practiced: The number of sample question papers the student practiced.
i will apply different machine learning models and find out which is most accurate ,do EDA , will do a compete data analysis , feature engineering etc. i will take input from user and do predict on Streamlit based Gui application.
This project aims to predict the estimated marks and grade of students based on various inputted factors using given dataset from Kaggle :
https://www.kaggle.com/datasets/nikhil7280/student-performance-multiple-linear-regression
This dataset includes columns like : Hours Studied: The total number of hours spent studying by each student. Previous Scores: The scores obtained by students in previous tests. Extracurricular Activities: Whether the student participates in extracurricular activities (Yes or No). Sleep Hours: The average number of hours of sleep the student had per day. Sample Question Papers Practiced: The number of sample question papers the student practiced.
i will apply different machine learning models and find out which is most accurate ,do EDA , will do a compete data analysis , feature engineering etc. i will take input from user and do predict on Streamlit based Gui application.