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I have a sequence dataset as the following.

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These sequences are statuses got approved by clients and they are ordered by date/time. A client can get multiple statuses and jump back to the same status again. Apart from this, I have data regarding, client's age, gender, nationality etc. The problem that I am trying to solve is to predict the next status of a client. I have used Python before. However, I am very new to data science and would like to bet approach follow. Thanks.

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    $\begingroup$Consider sequence prediction methods (e.g., Markov Chains, LSTMs, Transformers). encoding categorical features (e.g. age, gender), explore transition probabilities, and iteratively tune them (LSTM guide). If manual approval is costly, active learning (uncertainty sampling, EER) may help optimizing labelling. Further, client demographics might inform predictions; evaluating with temporal splits helps prevent leakage.$\endgroup$
    – Lynchian
    CommentedApr 2 at 9:36

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