Students Grade Predictions

About

This project involves predicting the final grades of students there are numerous features that were eventually prunned to a few to ensure that only relevant features gets feed into the Machine Learning model.

Features/Factors Considered/Independent Variable

The Features include everything ranging family size, parent's income, romantic life, availanility of internet services to first term and second term grade (G1 and G2)

Labels/Outcome/Dependent Variable

The label in question here is the G3 or final grades of the students.

Models

Two Main models were used in this project.

Linear Regression Random Forest Regression

Files Description

school_grades_dataset.csv - The Dataset used for this project gotten from kaggle https://www.kaggle.com/dipam7/student-grade-prediction

LabelBinarizedSGP.csv - The Dataset that has been filtered through the Label Binarizer algorithm to turn all columns that contains words into numerics.

LabelEncodedSGP.csv - The Dataset that has been filtered through the Label Encoder algorithm to turn the binarized columns into ones and zeroes

Student Grade Prediction using Random Forest Regressor.ipynb - Code for using Random Forest Regressor algorithm to predict the final grades.

Students Grades Prediction using linear regression.ipynb - Code for using Linear Regression algorithm to predict the final grades.