The mixed model approach for Categorical Sequence Time-series prediction

As we know, this is one of the hot topics and very hard to solve problem statement in the Data Science universe, I have come up with the solution when it comes to tackling categorical sequence prediction.

Without wasting further time lets get into this recipe:

- I derived my very own method of mixed model approaches.

- I used FBProphet and RandomForestClassifier ML algorithms

Process Flow [steps below]:

1. Encode categorical labels to numeric values.

2. Feed this numerical sequence to the FBProphet model. Tune the model based on your requirement.

3. Outptup of FBProphet is usually a trend distribution. Now map this output with the target column as your alphanumeric category.

4. Use the features and targets from step-3 to train the RandomforestClassifier.

I hope this will help you get the direction. I will keep updating this post. And will upload my Git repo soon…!

Before You Go

Thanks for reading! If you want to get in touch with me, feel free to reach me.

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Till then- keep Learning, Keep Sharing, Keep Growing.