Our primary objective was to explore the feasibility of predicting property prices based on their features. Using Selenium, we gathered data on property sales. We cleaned and refined the dataset, followed by data visualization, handling outliers, and finally we applied machine learning algorithms to ascertain the predictability of property prices.
active 2023-11-16 → 2023-11-16 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2023-11-16 to 2023-11-16. Pushes: 1 total, peak 1 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
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| NikitaBoro | 1 | 1 | 0 | 0 |
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.