iiitl / iiitl/Linear-Regression
Baseline Linear Regression with core metrics
Open
Beginner friendly
easy
good first issue
library
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 36
- PR merge metrics
- No merged PRs in 30d
Description
Train a baseline Linear Regression model with a fixed train-test split and random seed.
Report MAE, MSE, RMSE, and R2 on both train and test sets, with one short interpretation paragraph.
Contributor guide
Research direction
Start by locating the repository's Jupyter Notebook entry point and read the existing data and model setup. Train the baseline Linear Regression with a fixed train-test split and random seed, then report MAE, MSE, RMSE, and R2 for both sets with one short interpretation paragraph.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Quiet
- Clarity
- Clearly specified
- Newbie friendliness
- 64/100