As education has grown to rely more on technology, vast amounts of data has become available for examination and prediction. Logs of student activities, grades, interactions with teachers and fellow students, and more, are now captured in real time through learning management systems like Canvas and Edmodo. This is especially true for online classrooms, which are becoming popular even at the primary and secondary school level. Within all levels of education, there exists a push to help increase the likelihood of student success, without watering down the education or engaging in behaviors that fail to improve the underlying issues. Graduation rates are often the criteria of choice, and educators seek new ways to predict the success and failure of students early enough to stage effective interventions.
A local school district has a goal to reach a 95% graduation rate by the end of the decade by identifying students who need intervention before they drop out of school. As a software engineer contacted by the school district, your task is to model the factors that predict how likely a student is to pass their high school final exam, by constructing an intervention system that leverages supervised learning techniques. The board of supervisors has asked that you find the most effective model that uses the least amount of computation costs to save on the budget. You will need to analyze the dataset on students' performance and develop a model that will predict the likelihood that a given student will pass, quantifying whether an intervention is necessary.
This project uses the following software and Python libraries:
- Python 2.7
- NumPy
- pandas
- scikit-learn (v0.17)
You will also need to have software installed to run and execute a Jupyter Notebook.
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
For this assignment, you can find the student_intervention folder containing the necessary project files on the Machine Learning projects GitHub, under the projects folder. You may download all of the files for projects we'll use in this Nanodegree program directly from this repo. Please make sure that you use the most recent version of project files when completing a project!
This project contains two files:
student_intervention.ipynb: This is the main file where you will be performing your work on the project.student-data.csv: The project dataset. You?ll load this data in the notebook.
In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook student_intervention.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens. Follow the instructions in the notebook and answer each question presented to successfully complete the project. A README file has also been provided with the project files which may contain additional necessary information or instruction for the project.
Your project will be reviewed by a Udacity reviewer against the Building a Student Intervention System project rubric. Be sure to review this rubric thoroughly and self-evaluate your project before submission. All criteria found in the rubric must be meeting specifications for you to pass.
When you are ready to submit your project, collect the following files and compress them into a single archive for upload. Alternatively, you may supply the following files on your GitHub Repo in a folder named student_intervention for ease of access:
- The
student_intervention.ipynbnotebook file with all questions answered and all code cells executed and displaying output. - An HTML export of the project notebook with the name report.html. This file must be present for your project to be evaluated.
Once you have collected these files and reviewed the project rubric, proceed to the project submission page.
When you're ready to submit your project, click on the Submit Project button at the bottom of the page.
If you are having any problems submitting your project or wish to check on the status of your submission, please email us at [email protected] or visit us in the discussion forums.
You will get an email as soon as your reviewer has feedback for you. In the meantime, review your next project and feel free to get started on it or the courses supporting it!