Prediction of Salary From Profiles
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Transcript of Prediction of Salary From Profiles
Prediction of Salary from Profiles
-Sohom Ghosh
WEST BENGAL STATE SCIENCE AND TECHNOLOGY CONGRESS 2016 DATES :– 28th & 29th FEBRUARY, 2016 VENUE :– PRESIDENCY UNIVERSITY
DEPARTMENT :- COMPUTER SCIENCE
Motivation ( )
• Using Machine Learning to predict salaries that persons are likely to get by analyzing their profiles.
•
Why? ( ?)
• Negotiation for Salary ()
• Professional Training Institutes ( )
• Human Resource Department of Organizations (
)
• Recommendation to improve profile for drawing more salary (
)
Dataset & Features ( )
• Aspiring Minds’ Employment Outcomes 2015 dataset • Job seekers’ personal information (like gender, date of
birth etc.) • Pre-university information (like high school grades) • University information (like grade point, stream of
graduation, college reputation) • Demographic information (like college location) • Scores obtained in AMCAT's quantitative ability English
and logical reasoning section • Student’s first job location, designation and annual
salary
Implementation ( )
Raw Data Cleaning &
Preprocessing
• Replace Trivial Information like Student Id, DOB
• Categorical variables to numeric
• Feature Selection & Feature Engineering
Implementation ( )
Raw Data Cleaning &
Preprocessing Training &
Testing
• Decision Tree Regressor
• Logistic Regression
Discussions ( )
• The city in which a candidate is residing also impacts his salary
• Candidates from circuit related streams like Computer Science, Information Technology & Electronics draws more salary than core streams like Civil, Mechanical etc.
• Candidate’s score in English and his college gpa are largely responsible for his salary
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•
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Future Works & Improvements
• Recommender System which will help the students in standardizing their profile thereby assisting them to draw better salaries
• |
• Tableau (http://www.tableau.com/)
• Scikit Learn, Python (http://scikit-learn.org/)
• Aspiring Minds (http://www.aspiringminds.com/)
• http://ikdd.acm.org/Site/CoDS2016/downloaddataset.html
References ( )