Python Clustering Model For Sport Bet Prediction

I'm in need of a Python expert to develop a clustering model. This is a historical dataset with betting records from different accounts. Most accounts are not individual players but may belong to a specific group with similar betting behaviors. Please classify them accordingly. (Please check it in files callled [login to view URL]) I offered half of the ground truth, could use it for validation. but dont overfitting it is easy to tell since i will need the entire code. if you find it easily to train a model that works. that mean you are suitable, just dm me. Detail Rules: 1. Each miner_uid should belong to one and only one group. 2. If a group has fewer than three uids, merge those uids into an 'others' group. 3. There should be at least five groups, but no more than fifteen. 4. What I need is a uid-to-group mapping, a visualization of the classification results, the model training process, and the code for the model itself that can be used. 5. The task will be considered successfully completed if the clustering model achieves 80% accuracy to the ground truth I will provide for comparison. Key Requirements: - Basic data preprocessing: The model should handle basic preprocessing of the mixed data. - Algorithm flexibility: You can use any clustering algorithm you see fit (K-means, Hierarchical, DBSCAN, etc.) as long as it is accurate and does not overfit. - Model training visibility: I need complete access to the model training process to ensure the results are not due to overfitting. The compensation for this task is 30 USD. While this task is relatively simple, I currently lack the time to handle these smaller tasks, so I am outsourcing it.