19,158 candidate records across 14 demographic and professional features. The data maps features like city development index and education level to a binary target indicating job-seeking intent.
Use Cases
- Predict the target variable using the experience and training_hours columns to identify likely job seekers
- Use the city_development_index to analyze how regional economic conditions influence data scientist mobility
- Apply one-hot encoding to the company_type and major_discipline columns for use in a logistic regression model
Strengths
- 19,158 candidate records with 14 distinct feature columns
- Includes a city_development_index column representing the development level of the candidate's city
- Features a binary target column where 1 represents a candidate looking for a job change
- Contains categorical data for education_level, including 'Graduate', 'Masters', and 'Phd'