Skip to content

Implement a linear regression model to predict house prices using features such as square footage, the number of bedrooms, and bathrooms. This model will learn the relationship between these features and house prices, providing a way to estimate the price of a house given its characteristics.

active 2024-09-172024-09-28 (UTC)

Complete coverage27,171 / 27,173 hourly files (100%) · 2 absent upstream2023-08-152026-09-20 (UTC)
Events
10
Pushes
5
Pull requests
0
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 12 days from 2024-09-17 to 2024-09-28. Pushes: 5 total, peak 4 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
NadaEssam375500

Recent activity

Latest issues, pull requests and releases

No issue or PR events — this repo's activity is pushes only.

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.