This project is aimed at predicting a student's Semester Grade Point Average in the consequent semester based on the LINEAR REGRESSION . The two algorithms that are used are Gradient descent which is a first-order iterative optimization algorithm. Feature Normalization or Feature Scaling which is used to standardize the range of of independent variables. On running these algorithms against the dataset it was observed that there is a correlation between students abilities in individual semester with the final overall academic performance. It can be ascertained that the abilities and strong engineering fundamentals are strong factors in influencing the overall academic performance. This prediction model could be used by the student as an extensive guide to carefully pan out the effort that he/she must incorporate in order to score superlative grades in the next semester.
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This project is aimed at predicting a student's Semester Grade Point Average in the consequent semester based on the LINEAR REGRESSION . The two algorithms that are used are Gradient descent which is a first-order iterative optimization algorithm. Feature Normalization or Feature Scaling which is used to standardize the range of of independent v…
shaikkamran/VTU-STUDENTS-MARKS-PREDICTOR
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This project is aimed at predicting a student's Semester Grade Point Average in the consequent semester based on the LINEAR REGRESSION . The two algorithms that are used are Gradient descent which is a first-order iterative optimization algorithm. Feature Normalization or Feature Scaling which is used to standardize the range of of independent v…
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