Example 6.3: Assess a student's performance during his course of study and predict whether a student will get a job offer or not in his final year of the course. The training dataset T consists of 10 data instances with attributes such as 'CGPA', 'Interactiveness', 'Practical Knowledge' and 'Communication Skills' as shown in Table 6.3. The target class attribute is the 'Job Offer'.
Table 6.3: Training Dataset T
| S.No. | CGPA | Interactiveness | Practical Knowledge | Communication Skills | Job Offer |
|-------|-------|-----------------|--------------------|----------------------|-----------|
| 1. | ≥9 | Yes | Very good | Good | Yes |
| 2. | ≥8 | No | Good | Moderate | Yes |
| 3. | ≥9 | No | Average | Poor | No |
| 4. | <8 | No | Average | Good | No |
| 5. | ≥8 | Yes | Good | Moderate | Yes |
| 6. | ≥9 | Yes | Good | Moderate | Yes |
| 7. | <8 | Yes | Good | Poor | No |
| 8. | ≥9 | No | Very good | Good | Yes |
| 9. | ≥8 | Yes | Good | Good | Yes |
| 10. | ≥8 | Yes | Average | Good | Yes |
Answer (1)
To assess a student's performance and predict whether they will receive a job offer, we can utilize methods from machine learning and data mining, particularly classification techniques. Let's break down the process step-by-step:
Understanding the Dataset :
The dataset consists of 10 entries, where each entry has attributes related to a student's qualities: 'CGPA', 'Interactiveness', 'Practical Knowledge', and 'Communication Skills'.
The target variable we're trying to predict is 'Job Offer', which has binary outcomes: 'Yes' or 'No'.
Attributes :
CGPA : Represents the cumulative grade point average, which is grouped into >=9, >=8, <8.
Interactiveness : Whether the student participates in discussions and engages with content (Yes or No).
Practical Knowledge : Evaluated as Very good, Good, or Average.
Communication Skills : Evaluated as Good, Moderate, or Poor.
Analyzing Patterns :
From the table, we can observe that students with a CGPA >=9, high levels of Interactiveness, and at least Moderate Communication Skills tend to receive a job offer.
Practical Knowledge also plays a role, where higher practical skills usually align with receiving an offer.
Building a Model :
A simple Decision Tree or Rule-Based Classifier can be constructed using the patterns noted.
If CGPA >= 9 and Interactiveness = Yes and Communication Skills != Poor, then Job Offer = Yes.
Additional rules may be constructed to improve prediction accuracy.
Testing :
Apply this decision tree model to new data instances to predict the likelihood of receiving a job offer.
By employing such methods, one can systematically evaluate each factor's contribution to a job offer's likelihood, which can then guide both students and educational institutions in enhancing overall employability.
