https://github.com/anshpg/tsf-sp-linear
During my internship at The Sparks Foundation, I was tasked with predicting students' percentages based on the number of hours they studied.
https://github.com/anshpg/tsf-sp-linear
data-science linear-regression thesparksfoundationinternship
Last synced: over 1 year ago
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During my internship at The Sparks Foundation, I was tasked with predicting students' percentages based on the number of hours they studied.
- Host: GitHub
- URL: https://github.com/anshpg/tsf-sp-linear
- Owner: ANSHPG
- License: mit
- Created: 2024-05-13T18:31:23.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-13T18:47:10.000Z (about 2 years ago)
- Last Synced: 2025-01-21T11:32:13.323Z (over 1 year ago)
- Topics: data-science, linear-regression, thesparksfoundationinternship
- Language: Jupyter Notebook
- Homepage: https://colab.research.google.com/drive/1efnZ7AxZgq3J1lN3CPf1pfH8wdjq7sW_?usp=sharing
- Size: 75.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Personalized Linear Regression Approach for Student Performance Prediction
https://github.com/ANSHPG/TSF-SP-LINEAR/assets/132222062/e050904a-243a-40a7-bbe5-eb2d9188d0e5
During my internship at The Sparks Foundation, I was tasked with predicting students' percentages based on the number of hours they studied. This project involved a straightforward dataset with just two variables: hours and scores. Instead of relying on external libraries like scikit-learn, I opted to develop my own linear regression algorithm to tackle the task.
I'm pleased to share that through this approach, I successfully minimized the mean absolute error to 4.97, with an alpha (speed of convergence) of around 1.0e-2.
For a deeper insight into my methodology and implementation, I've provided a link to the GitHub repository.
github_repository: https://github.com/ANSHPG/LinearLuminary
Thank you for your interest!