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Human Resources Classification Data
Data Science and Analytics
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About
The data contains records of individuals applying for a specialist Gold Digger role. Its primary function is to serve as a structured environment for binary classification challenges. Data scientists can use applicant features—such as age, educational level, examination scores, and experience—to build robust classification models aimed at predicting the target variable, which indicates if the candidate was successfully hired or not.
Columns
- date (date): The specific date when the application was formally submitted.
- age (age): The recorded age of the candidate upon application.
- diplome (degree): The highest academic qualification obtained (e.g., bac, licence, master, doctorat).
- specialite (minor): The specialised minor area of study associated with the diploma (e.g., geologie, forage, detective, archeologie).
- salaire (salary): The expected salary stated by the applicant.
- dispo (availability): A binary field indicating if the candidate is directly available (oui) or not (non).
- sexe (sex): The candidate's recorded sex (F for female, M for male).
- exp (experience): The number of years of relevant professional experience.
- cheveux (hair): The hair colour of the applicant (e.g., chatain, brun, blond, roux).
- note (grade): The score achieved by the candidate on the standardised gold digging examination (out of 100).
- embauche (hired): The target variable; a binary indicator (1 for hired, 0 for not hired).
Distribution
The dataframe comprises 20,000 distinct observations and 11 distinct features. The data is suitable for large-scale analysis and model training, presented typically in a CSV file format. The target variable, embauche (hired), is fully populated with no missing records. The mean age of applicants is approximately 35 years, while the average relevant experience stands at 9.5 years. Salary expectations average around 35,000 units. The applicant pool is predominantly male (59%), and the most frequent qualification level is the master's degree (38%).
Usage
This data product is perfectly suited for several analytical and skill development scenarios, including:
- Building and evaluating binary classification models (e.g., Logistic Regression, Decision Trees, Gradient Boosting).
- Performing detailed Exploratory Data Analysis (EDA) on applicant demographics and performance trends.
- Practising feature engineering and data cleaning techniques on recruitment data.
- Serving as a practical case study for aspiring data scientists preparing for interviews.
- Benchmarking different model performances for predictive accuracy in human resources contexts.
Coverage
The dataset captures applications submitted over a five-year period, specifically ranging from 1 January 2010 through 31 December 2014. It covers 20,000 applicants, detailing their age, sex, education level, experience, and performance scores. Geographically, the source of the data is implied by the nature of the application process, focusing on the specific recruitment cycle for this particular position.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For developing, training, and testing predictive classification models.
- Machine Learning Engineers: For benchmarking algorithm performance and fine-tuning model parameters.
- Students: For educational purposes, learning data visualisation, and practicing foundational data science skills.
- Recruitment Analysts: To simulate hiring criteria and evaluate the impact of different candidate attributes on selection success.
Dataset Name Suggestions
- Gold Digger Recruitment Classification
- Applicant Hiring Prediction Challenge
- Human Resources Classification Data
- Candidate Profile Scorecard
Attributes
Original Data Source: Human Resources Classification Data
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