Week 1 — Career Path Predictor

Hello everyone, from a new day where AI makes our lives better, we are here today with a project that will shape your future. We wish everyone a pleasant reading.

Brief Overview Of The Project

We aim to create a recommadation system using artificial intelligence algorithms for people who are studying in computer society and relative fields or who have just started in this field so that they can direct their career goals correctly. The biggest factor in choosing this project is our desire to help people who have lost their motivation and despair because they have not yet decided on their career path. We aim to create different machine learning algorithms with the examples in the existing dataset and reach the best prediction results. In this way, you can learn your career path by using our model and get rid of your indecisions that have lasted for centuries.

Dataset

Our dataset has 19 features, 1 label and 6901 observations. As it can be understood from the presence of a label in our dataset, this study was planned as an example of a supervised learning model. Within the features, there is information about both experience and the interests of the observed, such as certifications, interesting career area, hackathons. This information is available in categorical and numerical forms.

Our dataset[1] is available via GitHub link.

Related Works

We found three studies related to our topic. The first of these is ‘Computer Science Career Recommendation System Using Artificial Neural Network’[2], which was done by Brijmohan Daga, Juhi Checker,Anne Rajan, Sayali Deo from Department of Computer Engineering, Fr. Conceicao Rodrigues College of Engineering, Bandra West, Mumbai-400050, India. This paper proposes an automated system using an Artificial Neural Network which considers personality traits of the individual along with personal interests and academics to predict which computer science job role would be best suited for them. The second study ‘A Machine Learning Approach for Future Career Planning’[3] was done by Yu Lou , Ran Ren and Yiyang Zhao from Stanford University Computer Science and its about modeling people’s career developments with Markov Chain, and present the approach to estimate the transition probability matrix. The last study we found was ‘Skill-based Career Path Modeling and Recommendation’[4] was done by Aritra Ghosh, Beverly Woolf, Shlomo Zilberstein, Andrew Lan from College of Information and Computer Sciences, University of Massachusetts Amherst. In this project , using a series of experimentson two large real-world datasets, they show that the model (sometimes significantly) outperforms existing methods on the tasks of company, job title, and skill prediction.

We are very excited for this project. I hope you will enjoy being included. See you next week!

• Melike Nur Dulkadir
• Sare Naz Ersoy

References

[1]https://raw.githubusercontent.com/Umang-19/devjam/main/public/mldata.csv
[2]https://ijcttjournal.org/2020/Volume-68%20Issue-3/IJCTT-V68I3P117.pdf
[3]http://cs229.stanford.edu/proj2010/LouRenZhangAMachineLearningApproachForFutureCareerPlanning.pdf
[4]https://people.umass.edu/~andrewlan/papers/20bigdata-mnss.pdf