ML-Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!ππ« Devfolio URL, https://devfolio.co/projects/mlcrate-98f9
Imported essential libraries for data manipulation and machine learning.
Conducted Exploratory Data Analysis (EDA) to comprehend the dataset.
Visualized data to extract meaningful patterns and insights.
Setup a function to assign Sentiment to an Event
Assessed feature correlations to understand interdependencies.
Converted categorical features into numerical formats via feature mapping.
Split the dataset into training and testing sets and applied scaling techniques.
Implemented and trained four machine learning models: Random Forest, SVM, Logistic Regression, and Gradient Booster.
Evaluated the models using classification reports and compared their accuracies to determine the best-performing model.
Type of change βοΈ
What sort of change have you made:
[x] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[x] Code style update (formatting, local variables)
[x] Breaking change (fix or feature that would cause existing functionality to work as expected)
How Has This Been Tested? βοΈ
Among all the models tested, the Gradient Booster achieved the highest accuracy, approximately 100%, making it the best-performing model for predicting gold prices. This demonstrates its effectiveness in handling the dataset and providing reliable predictions.
Checklist: βοΈ
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my code.
[x] I have commented on my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.
Pull Request for ML-Crate π‘
Issue Title: The Effect of Economic News on Gold Prices Analysis
Closes: #507
Describe the add-ons or changes you've made π
Type of change βοΈ
What sort of change have you made:
How Has This Been Tested? βοΈ
Among all the models tested, the Gradient Booster achieved the highest accuracy, approximately 100%, making it the best-performing model for predicting gold prices. This demonstrates its effectiveness in handling the dataset and providing reliable predictions.
Checklist: βοΈ