ctrl-gaurav / amazon-ml-challenge-2023

Amazon ML Challenge 2023 Solution

Problem Statement

The problem statement for this round of the Amazon ML Challenge 2023 was to predict the length of a product based on several features, including product category, product dimensions, and seller information.

Approach

We approached this problem by using a gradient boosting machine (GBM) algorithm to train our machine learning model. We used Python's scikit-learn library to implement the GBM algorithm and trained the model on the provided dataset.

To improve the accuracy of our model, we performed feature engineering to select the most relevant features and remove any redundant or irrelevant ones. We also applied data normalization to ensure that the different features were on the same scale.

Once we had trained our model, we used it to predict the prices of the test dataset provided by Amazon. Our model achieved an score of 35.129 on the test dataset, which gave us the rank of 118 among all participants in the competition.

Future Improvements

While we are proud of our results, we recognize that there is always room for improvement. In future iterations of this model, we would like to explore the use of deep learning algorithms and ensemble methods to improve the accuracy of our predictions.

Conclusion

The Amazon ML Challenge 2023 was an exciting opportunity for us to showcase our skills in machine learning and compete with other talented individuals. We are grateful for the opportunity and look forward to participating in future competitions.

Thank you for taking the time to review our solution. If you have any questions or feedback, please do not hesitate to reach out to us.