RyanS974/510app_model_rf
Text Classification
•
Updated
Datasets:
id
int64 1
1k
| skills
stringclasses 151
values | exp
int64 0
5
| grades
float64 60.1
99.9
| projects
int64 0
9
| extra
int64 0
4
| offer
int64 0
1
| hire
stringclasses 3
values | pay
stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|
1
|
Python;Data Analysis;SQL
| 3
| 75.26
| 9
| 0
| 1
|
No
|
NoPay
|
2
|
Java
| 4
| 74.25
| 6
| 2
| 1
|
No
|
NoPay
|
3
|
Data Analysis
| 2
| 74.89
| 4
| 3
| 0
|
No
|
NoPay
|
4
|
Data Analysis
| 4
| 72.73
| 2
| 3
| 1
|
No
|
NoPay
|
5
|
Machine Learning;Python;C++
| 4
| 84.85
| 1
| 4
| 0
|
Interview
|
NoPay
|
6
|
Python
| 1
| 66.57
| 7
| 3
| 1
|
No
|
NoPay
|
7
|
Java
| 2
| 93.84
| 1
| 3
| 0
|
No
|
NoPay
|
8
|
Machine Learning
| 2
| 92.5
| 7
| 2
| 0
|
No
|
NoPay
|
9
|
Python;Machine Learning;Java
| 2
| 79.06
| 4
| 1
| 0
|
Yes
|
125
|
10
|
Data Analysis;Machine Learning;C++
| 4
| 94.11
| 8
| 3
| 1
|
No
|
NoPay
|
11
|
C++
| 3
| 69.89
| 3
| 1
| 0
|
No
|
NoPay
|
12
|
SQL;Python;Java
| 2
| 60.7
| 7
| 0
| 0
|
No
|
NoPay
|
13
|
C++;SQL;Machine Learning
| 5
| 84.77
| 6
| 3
| 0
|
No
|
NoPay
|
14
|
Java
| 4
| 78.32
| 7
| 0
| 0
|
No
|
NoPay
|
15
|
Python;Data Analysis
| 1
| 82.69
| 4
| 0
| 1
|
No
|
NoPay
|
16
|
Python;SQL
| 3
| 95.97
| 1
| 4
| 1
|
No
|
NoPay
|
17
|
Machine Learning;SQL
| 5
| 98.8
| 6
| 1
| 0
|
No
|
NoPay
|
18
|
Data Analysis
| 5
| 85.38
| 2
| 1
| 1
|
No
|
NoPay
|
19
|
Machine Learning;Python
| 1
| 90.56
| 9
| 4
| 1
|
Yes
|
150
|
20
|
Python;Machine Learning
| 3
| 63.35
| 9
| 2
| 0
|
No
|
NoPay
|
21
|
Data Analysis;Machine Learning
| 4
| 66.43
| 9
| 4
| 0
|
No
|
NoPay
|
22
|
Machine Learning;Java
| 0
| 89.33
| 9
| 4
| 1
|
No
|
NoPay
|
23
|
Python;Data Analysis;Java
| 3
| 77.25
| 1
| 4
| 1
|
No
|
NoPay
|
24
|
Python;Java
| 1
| 77.84
| 2
| 0
| 1
|
No
|
NoPay
|
25
|
C++
| 5
| 72.34
| 3
| 1
| 1
|
No
|
NoPay
|
26
|
C++;SQL
| 4
| 84.29
| 9
| 2
| 1
|
No
|
NoPay
|
27
|
SQL
| 3
| 67.41
| 6
| 3
| 1
|
No
|
NoPay
|
28
|
Java;SQL
| 0
| 85.08
| 9
| 3
| 0
|
No
|
NoPay
|
29
|
Data Analysis;Python;Java
| 0
| 66.95
| 0
| 3
| 1
|
No
|
NoPay
|
30
|
Data Analysis;Java;Machine Learning
| 2
| 62.55
| 5
| 2
| 0
|
No
|
NoPay
|
31
|
C++;SQL
| 2
| 73.21
| 5
| 3
| 0
|
No
|
NoPay
|
32
|
Data Analysis;Machine Learning;Java
| 1
| 69.25
| 7
| 2
| 1
|
No
|
NoPay
|
33
|
SQL;Python;Java
| 3
| 95.56
| 1
| 3
| 1
|
No
|
NoPay
|
34
|
SQL
| 3
| 62.2
| 1
| 3
| 0
|
No
|
NoPay
|
35
|
SQL;Python
| 5
| 67.06
| 0
| 2
| 1
|
No
|
NoPay
|
36
|
Machine Learning
| 5
| 81.95
| 2
| 1
| 1
|
No
|
NoPay
|
37
|
SQL;Java;C++
| 5
| 79.24
| 6
| 2
| 0
|
No
|
NoPay
|
38
|
Data Analysis;C++
| 2
| 61.83
| 7
| 2
| 1
|
No
|
NoPay
|
39
|
SQL
| 3
| 60.29
| 6
| 4
| 0
|
No
|
NoPay
|
40
|
Java
| 3
| 99.52
| 8
| 0
| 0
|
No
|
NoPay
|
41
|
Machine Learning;Data Analysis;SQL
| 0
| 86.35
| 7
| 0
| 1
|
No
|
NoPay
|
42
|
Machine Learning;C++;Data Analysis
| 2
| 74.62
| 7
| 1
| 0
|
No
|
NoPay
|
43
|
Java;Data Analysis
| 4
| 78.82
| 1
| 1
| 0
|
No
|
NoPay
|
44
|
C++;Python;Machine Learning
| 2
| 70.66
| 0
| 1
| 0
|
No
|
NoPay
|
45
|
Java
| 4
| 73.79
| 5
| 0
| 1
|
No
|
NoPay
|
46
|
Java;C++;Python
| 0
| 85.96
| 6
| 1
| 1
|
No
|
NoPay
|
47
|
C++;Machine Learning
| 1
| 95.31
| 5
| 1
| 1
|
No
|
NoPay
|
48
|
Machine Learning;SQL
| 3
| 67.37
| 6
| 2
| 1
|
No
|
NoPay
|
49
|
Python;Data Analysis;SQL
| 0
| 93.61
| 7
| 2
| 0
|
No
|
NoPay
|
50
|
SQL;Python;Data Analysis
| 3
| 64.86
| 1
| 0
| 0
|
No
|
NoPay
|
51
|
Java;C++
| 5
| 68.6
| 0
| 2
| 1
|
No
|
NoPay
|
52
|
Data Analysis
| 1
| 61.49
| 9
| 1
| 0
|
No
|
NoPay
|
53
|
SQL;Machine Learning;Java
| 1
| 65.63
| 7
| 2
| 0
|
No
|
NoPay
|
54
|
Python;SQL;Java
| 0
| 96.69
| 7
| 4
| 1
|
No
|
NoPay
|
55
|
SQL
| 1
| 78.38
| 7
| 2
| 1
|
No
|
NoPay
|
56
|
Machine Learning
| 4
| 63.11
| 9
| 0
| 1
|
No
|
NoPay
|
57
|
Python;Machine Learning;SQL
| 1
| 98.39
| 6
| 1
| 0
|
Yes
|
125
|
58
|
Python;Data Analysis
| 3
| 95.16
| 1
| 1
| 1
|
No
|
NoPay
|
59
|
SQL;Java
| 3
| 90.19
| 3
| 0
| 1
|
No
|
NoPay
|
60
|
Java
| 3
| 97.34
| 8
| 2
| 0
|
No
|
NoPay
|
61
|
Python;Data Analysis;C++
| 3
| 68.81
| 7
| 1
| 1
|
No
|
NoPay
|
62
|
Machine Learning
| 4
| 93.45
| 3
| 1
| 1
|
No
|
NoPay
|
63
|
Java
| 2
| 87.6
| 7
| 4
| 0
|
No
|
NoPay
|
64
|
C++;Machine Learning;Java
| 5
| 94.79
| 9
| 0
| 1
|
No
|
NoPay
|
65
|
Machine Learning;Data Analysis
| 0
| 91.88
| 5
| 2
| 1
|
No
|
NoPay
|
66
|
Java;Machine Learning
| 3
| 73.86
| 8
| 2
| 0
|
No
|
NoPay
|
67
|
Data Analysis;Java;SQL
| 1
| 90.98
| 0
| 1
| 1
|
No
|
NoPay
|
68
|
Data Analysis;SQL
| 3
| 70.5
| 5
| 3
| 1
|
No
|
NoPay
|
69
|
Machine Learning;C++
| 1
| 73.14
| 3
| 4
| 0
|
No
|
NoPay
|
70
|
Java
| 5
| 64.32
| 2
| 4
| 0
|
No
|
NoPay
|
71
|
Python
| 5
| 97.4
| 4
| 4
| 1
|
No
|
NoPay
|
72
|
Python;Machine Learning
| 5
| 71.64
| 9
| 1
| 0
|
No
|
NoPay
|
73
|
Java;Machine Learning;Data Analysis
| 1
| 80.82
| 7
| 0
| 0
|
No
|
NoPay
|
74
|
Python
| 3
| 75.65
| 9
| 0
| 1
|
No
|
NoPay
|
75
|
Data Analysis;Python;Java
| 5
| 98.4
| 5
| 4
| 0
|
No
|
NoPay
|
76
|
Python
| 4
| 73.4
| 0
| 3
| 1
|
No
|
NoPay
|
77
|
Python;Machine Learning
| 1
| 91.14
| 5
| 3
| 1
|
Yes
|
125
|
78
|
SQL
| 1
| 94.22
| 6
| 2
| 0
|
No
|
NoPay
|
79
|
C++;Java;Machine Learning
| 3
| 77.42
| 5
| 3
| 0
|
No
|
NoPay
|
80
|
Machine Learning;Data Analysis;C++
| 1
| 75.13
| 2
| 4
| 0
|
No
|
NoPay
|
81
|
C++
| 1
| 89.91
| 8
| 2
| 1
|
No
|
NoPay
|
82
|
Java;Python
| 5
| 96.52
| 9
| 4
| 0
|
No
|
NoPay
|
83
|
Data Analysis
| 3
| 90.2
| 9
| 0
| 1
|
No
|
NoPay
|
84
|
SQL;C++
| 5
| 69.09
| 3
| 4
| 0
|
No
|
NoPay
|
85
|
C++;Python
| 5
| 95.53
| 4
| 2
| 1
|
No
|
NoPay
|
86
|
Data Analysis;Python;Machine Learning
| 3
| 75.5
| 4
| 4
| 1
|
Yes
|
125
|
87
|
Data Analysis;SQL
| 0
| 83.05
| 2
| 1
| 0
|
No
|
NoPay
|
88
|
Java
| 5
| 93.26
| 1
| 2
| 1
|
No
|
NoPay
|
89
|
Java
| 4
| 67.54
| 7
| 3
| 1
|
No
|
NoPay
|
90
|
C++;Java;Data Analysis
| 4
| 91.38
| 6
| 0
| 1
|
No
|
NoPay
|
91
|
SQL
| 1
| 83.85
| 4
| 3
| 0
|
No
|
NoPay
|
92
|
Java;Python
| 4
| 95.73
| 0
| 4
| 0
|
No
|
NoPay
|
93
|
Machine Learning;Python
| 1
| 66.88
| 9
| 3
| 0
|
No
|
NoPay
|
94
|
Data Analysis
| 0
| 76.83
| 0
| 2
| 0
|
No
|
NoPay
|
95
|
Python;Data Analysis;Java
| 3
| 93.31
| 7
| 1
| 0
|
No
|
NoPay
|
96
|
C++
| 3
| 60.77
| 3
| 3
| 0
|
No
|
NoPay
|
97
|
C++;Java
| 3
| 64.49
| 3
| 4
| 0
|
No
|
NoPay
|
98
|
Python;Java
| 4
| 99.62
| 8
| 3
| 1
|
No
|
NoPay
|
99
|
Python;C++
| 0
| 71.22
| 1
| 3
| 1
|
No
|
NoPay
|
100
|
C++;SQL
| 4
| 99.02
| 4
| 2
| 0
|
No
|
NoPay
|
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Dataset Card for Job Fair Candidates Classification Dataset
A supervised learning dataset for multi-label classification in tech industry hiring, focusing on candidate evaluation and salary prediction.
Dataset Details
Dataset Description
A specialized dataset created for supervised learning tasks in hiring prediction. The dataset contains candidate information with 7 features and 2 classification labels, derived from a larger unlabeled dataset. This dataset contains fictitious candidate data and is intended for educational purposes.
- Curated by: Ryan Smith
- Language(s) (NLP): English
- License: Apache 2.0
Dataset Sources
CodaLab Page
Uses
Direct Use
This dataset is suitable for multi-label classification tasks in HR analytics and candidate evaluation, specifically for developing predictive models for hiring decisions and salary ranges in the tech industry.
Out-of-Scope Use
This dataset should not be used for making automated hiring decisions without human oversight. It is intended for educational and research purposes only, and should not be the sole basis for actual hiring decisions.
Dataset Structure
The dataset includes the following features and labels:
Features:
- id - Candidate identifier
- skills - Candidate's technical skills
- exp - Work experience
- grades - Academic grades
- projects - Project experience
- extra - Extra curricular activities
- offer - Previous job offers
Labels:
- hire - Hiring decision
- Yes
- No
- Interview
- pay - Salary classification
- NoPay
- 125
- 150
Dataset Creation
Curation Rationale
This dataset was created as part of a school project focusing on supervised learning, specifically multi-label classification. The primary goal was to develop predictive models for tech industry hiring decisions and salary ranges.
Source Data
Data Collection and Processing
The dataset is derived from the original Kaggle Job Fair Candidates dataset. Labels were added through a structured annotation process using spreadsheets. The data is entirely fictitious and was created for educational purposes.
Who are the source data producers?
The original dataset was sourced from Kaggle, with modifications and labeling done for educational purposes.
Annotations
Annotation process
- Three master's degree students served as expert annotators
- Labels were added using a spreadsheet-based process
- Majority rule system was implemented for label determination
- The use of three annotators eliminated the possibility of disputed labels
Who are the annotators?
The annotators were master's degree students with relevant academic background in the field.
Personal and Sensitive Information
The dataset contains fictitious information only. No real personal data is included in this dataset.
Bias, Risks, and Limitations
Users of this dataset and any models derived from it assume all risks related to job hiring success or failure. This dataset should be used as one of many tools in the hiring decision process, not as the sole decision-maker.
Recommendations
Users should be made aware that:
- This dataset contains fictitious data created for educational purposes
- Models trained on this data require human oversight
- The dataset should be used as one of many tools in hiring decisions
- Results should be validated against specific use cases
Dataset Card Authors
Ryan Smith
Dataset Card Contact
- Downloads last month
- 3
Size of downloaded dataset files:
45.3 kB
Size of the auto-converted Parquet files:
19.2 kB
Number of rows:
1,000
Models trained or fine-tuned on RyanS974/510app_dataset