Treffer: Mentor Recommendation

Dataset Description: IT Mentor-Mentee Matching Dataset Overview The IT Mentor-Mentee Matching Dataset is a comprehensive collection of profiles designed to facilitate mentor-mentee relationships within the information technology sector. This dataset is based on information gathered from a nonprofit organization in the U.S. that focuses on mentorship initiatives aimed at empowering individuals in the IT field. Structure The dataset comprises 5,000 rows of profiles, each characterized by the following columns: User_ID: Unique identifier for each mentor or mentee. Position: The job title (e.g., Front-End Developer, Data Scientist) relevant to the user. Experience_Level: A categorical variable indicating the user’s level of experience (e.g., Entry, Mid, Senior). Years_of_Experience: Numeric representation of the years spent in the industry. Primary_Language: The primary programming or scripting language the user is proficient in (e.g., Python, JavaScript). Secondary_Languages: A list of additional programming languages the user is familiar with. Expert_Roles: Key areas of expertise within the user’s field (e.g., React, Machine Learning). Industry: The industry context in which the user operates (e.g., Finance, Healthcare, Education). Education: The highest level of education attained by the user. Availability: The user’s availability for mentoring or mentoring sessions (e.g., Part-time, Full-time). Purpose This dataset aims to support research and development in mentor-mentee matching systems, enabling organizations, educational institutions, and individual professionals to foster mentorship opportunities that enhance skills and career development within the IT industry. The dataset serves as a valuable resource for various stakeholders seeking to promote growth and knowledge sharing in the tech community. Use Cases Mentor-Mentee Matching: Create algorithms to effectively match mentors with mentees based on skills, experience, and availability. Collaborative Filtering: Implement machine learning models that predict ...