Students have three terms in secondary school and tend to either pass more or fail more in some terms.
There are diferent factors that contribute to students failure but unfortunately most times we term it as unseriosness of the student which is not the case in m,any cases.
This projects centers on building a predictive machine learning app to predict whether or not a student will pass at the end of the term and how well a student would pass or fail.
The app should also tell the reason for the prediction and how to work on getting better.
The dataset was in a tabular (CSV) format and was gotten from kaggle Here, which was subsequently cleaned and wrangled in preparation for machine learning.
- Programming (Python, JavaScript)
- Data wrangling (Pandas, Numpy)
- Data Analysis and Visualization. (Numpy, Stat, Seaborn, Matplotlib)
- Machine/ Deep learning (Tensorflow, Scikit Learn, XGBoost)
- Backend (Flask)
- Frontend (HTML, CSS, Bootstrap)
- Cloud deployment (Render, Heroku)
 |
This Jupyter notebook containing some exploratory analysis, model training and evaluation can be found Here
- Has a section to fill form to collect data.
- Machine learning predicts whether or not a student will pass at the end of the term and how much he/she will pass/fail.
- The app also tells the reason for the prediction and how to work on getting better.
- Web app compatible by every device.
This app is deployed at Render
You can access it Here
- The dataset used was not large enough, could be outdated and can't be said to have generalized well despite the high metric values.
- The app's interface could be better.
- Render makes it slow for the app to load.
- I would love to improve the dataset in size and quality.
- I would love to add an NLP virtual assistant system in form of a chatbot to attend to the user and give better ways to perform well in subsequent terms.
You can create a pull request wit detailed explanation if you wiould love to work more on this, or contact me through:
