Tekijä: Daniel Plăcintă
An extremely important step in every student's personal development is admission to a prestigious, elite high school.
Develop a model that predicts whether, based on multiple results, the candidate will be admitted to an elite high school (status_admitere = 1) or rejected (status_admitere = 0).
You will train the model using the training dataset train_data.csv and then generate predictions using the test dataset test_data.csv.
Training Dataset (train.csv) contains the following columns:
Prediction Dataset (test.csv):
id, gen, judet, NT, MEV, MATE, MGIM) as the training set, without the status_admitere column.The submission file output.csv must contain exactly three columns: subtaskID, datapointID, answer.
| subtaskID | datapointID | answer | Description |
|---|---|---|---|
| 1 | 101 | 1.25 | dif_NT-MEV: difference between admission test score and national evaluation score, with 2 decimal places |
| 2 | 101 | 5 | loc-MEV: ranking position by MEV score, integer value |
| 3 | 101 | 1 | status_admitere: model prediction, 1 = admitted, 0 = rejected |
Important: Each row in the CSV represents the answer for a single subtask and a single datapoint. For each
datapointIDthere must be one row for each subtask.
Submitting the sample_output.csv file generates 5 points.