Gold price prediction added #682
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. To help move the process along, please tag @UppuluriKalyani, @Neilblaze, and @SaiNivedh26 for a faster review! |
github-actions
bot
requested review from
Neilblaze,
SaiNivedh26 and
UppuluriKalyani
UppuluriKalyani
approved these changes
Oct 30, 2024
|
🎉🎉 Thank you for your contribution! Your PR #682 has been merged! 🎉🎉 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Related Issue
This PR addresses
Issue #680, which proposed adding a gold price prediction model to the ML Nexus project.
Fixes: #680
Proposed Changes
1.Added a Gold Price Prediction model using the Random Forest Regressor.
2.Achieved an R² score of 0.9898, ensuring high prediction accuracy.
3.Implemented visualizations for historical data trends and future price forecasts.
4.Created a modular and reusable code structure.