Student Exam Score Predictions

This is a Multiple Linear Regression (MLR) model that predicts exam scores of fictitious high school students based on several external factors (like parental level of education). The dataset used for this model can be found HERE.

Setup

To obtain the latest version of pip, input the following command in your terminal:

$ pip3 install --upgrade pip

Using pip, install Jupyter, pandas, Matplotlib, and scikit-learn one after another.

$ pip3 install jupyter
$ pip3 install pandas
$ pip3 install matplotlib
$ pip3 install scikit-learn

To ensure that each of the aforementioned packages is installed properly, input the following command:

$ pip3 list

Installation

Now, git clone this repository, and navigate to the student-exam-scores-predictions directory.

$ git clone https://github.com/CS-4412-Data-Mining/student-exam-scores-predictions.git
$ cd student-exam-scores-predictions

Usage

First, open basic_visualizations.ipynb to view different visualizations of the StudentsPerformance.csv dataset.

$ jupyter notebook basic_visualizations.ipynb

Next, navigate to http://localhost:8888/notebooks/basic_visualizations.ipynb in the browser, and press the ⏩ button to restart the kernel and rerun the whole notebook.

Second, open test_score_regression.ipynb to observe the MLR model.

$ jupyter notebook test_score_regression.ipynb

Next, navigate to http://localhost:8888/notebooks/test_score_regression.ipynb in the browser, and press the ⏩ button to restart the kernel and rerun the whole notebook.