This is a Udacity machine learning nanodegree project deliverable, please use in accordance with Udacity honor code.
The goal of this project is to model the factors that predict how likely a student is to pass high school final exam.
The 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.
The model will be evaluated on three factors:
- F1 score, summarizing the number of correct positives and correct negatives out of all possible cases. In other words, how well does the model differentiate likely passes from failures?
- The size of the training set, preferring smaller training sets over larger ones. That is, how much data does the model need to make a reasonable prediction?
- The computation resources to make a reliable prediction. How much time and memory is required to correctly identify students that need intervention?
The following SW was used in the first part of the project:
- Python 2.7
- NumPy
- scikit-learn
- iPython Notebook
In the last part of this project, R was used as an EDA tool:
- R 3.2.3
- ggplot
Dataset is included in this repository as csv file: student-data.csv. more details about the dataset can be found in the final report.
Final report and IPython notebook are included in this repository. IPython notebook is straightforward to use, please refer to http://cs231n.github.io/ipython-tutorial/ for a quick tutorial.