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Candidate Hiring Factors Dataset
Data Science and Analytics
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About
This dataset offers valuable insights into the factors influencing hiring decisions within recruitment processes. Each record represents a candidate with various attributes considered during the hiring journey. The primary objective is to enable the prediction of whether a candidate will be hired based on these attributes. It serves as an excellent resource for exploring machine learning models and strategies aimed at optimising recruitment processes in diverse organisational contexts.
Columns
- Age: The age of the candidate. Data type: Integer. Data range: 20 to 50 years.
- Gender: The gender of the candidate. Data type: Binary. Categories: Male (0) or Female (1).
- Education Level: The highest level of education attained by the candidate. Data type: Categorical. Categories: Bachelor's (Type 1), Bachelor's (Type 2), Master's, PhD.
- Experience Years: The number of years of professional experience. Data type: Integer. Data range: 0 to 15 years.
- Previous Companies Worked: The number of previous companies where the candidate has worked. Data type: Integer. Data range: 1 to 5 companies.
- Distance From Company: The distance in kilometres from the candidate's residence to the hiring company. Data type: Float (continuous). Data range: 1 to 50 kilometres.
- Interview Score: The score achieved by the candidate in the interview process. Data type: Integer. Data range: 0 to 100.
- Skill Score: The assessment score of the candidate's technical skills. Data type: Integer. Data range: 0 to 100.
- Personality Score: The evaluation score of the candidate's personality traits. Data type: Integer. Data range: 0 to 100.
- Recruitment Strategy: The strategy adopted by the hiring team for recruitment. Data type: Categorical. Categories: Aggressive (1), Moderate (2), Conservative (3).
- Hiring Decision (Target Variable): The outcome of the hiring decision. Data type: Binary (Integer). Categories: Not hired (0) or Hired (1).
Distribution
The dataset contains 1500 records with 10 features and one target variable. The target variable, 'Hiring Decision', is binary. The data file is typically provided in CSV format and is approximately 63.96 kB in size, comprising 11 columns.
Usage
This dataset is ideal for data science and machine learning projects. It can be utilised to:
- Develop and test machine learning models for predicting hiring outcomes.
- Gain insights into crucial factors influencing hiring decisions.
- Optimise recruitment processes and strategies within various organisational contexts.
Coverage
The demographic scope of the dataset includes candidates aged 20 to 50 years, along with specified gender and education levels. The dataset does not specify a geographic or time range.
License
Attribution 4.0 International (CC BY 4.0) license
Who Can Use It
- Data Scientists and Machine Learning Engineers: For building predictive models, developing algorithms, and conducting analytical studies related to recruitment.
- HR Professionals and Recruitment Specialists: To analyse factors impacting hiring decisions and to refine their recruitment strategies.
- Students and Researchers: For educational purposes, academic studies, and exploring applications of data science in human resources and business analytics.
Dataset Name Suggestions
- Recruitment Decision Predictor Data
- Candidate Hiring Factors Dataset
- HR Predictive Hiring Data
- Synthetic Recruitment Outcomes Data
Attributes
Original Data Source: Candidate Hiring Factors Dataset
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