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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.08071 |
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| _version_ | 1866911914327539712 |
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| author | Patel, Zalak Porwal, Ayushi Bhandare, Kajal Woo, Jongwook |
| author_facet | Patel, Zalak Porwal, Ayushi Bhandare, Kajal Woo, Jongwook |
| contents | This paper will illustrate the usage of Machine Learning algorithms on US College Scorecard datasets. For this paper, we will use our knowledge, research, and development of a predictive model to compare the results of all the models and predict the public and private net prices. This paper focuses on analyzing US College Scorecard data from data published on government websites.
Our goal is to use four machine learning regression models to develop a predictive model to forecast the equitable net cost for every college, encompassing both public institutions and private, whether for-profit or nonprofit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | US College Net Price Prediction Comparing ML Regression Models Patel, Zalak Porwal, Ayushi Bhandare, Kajal Woo, Jongwook Computers and Society This paper will illustrate the usage of Machine Learning algorithms on US College Scorecard datasets. For this paper, we will use our knowledge, research, and development of a predictive model to compare the results of all the models and predict the public and private net prices. This paper focuses on analyzing US College Scorecard data from data published on government websites. Our goal is to use four machine learning regression models to develop a predictive model to forecast the equitable net cost for every college, encompassing both public institutions and private, whether for-profit or nonprofit. |
| title | US College Net Price Prediction Comparing ML Regression Models |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2406.08071 |