fraud-detection | Fraud Detection model build with Python | Machine Learning library

This is very likely to be a versioning problem. Your scikit-learn version is probably the latest version and the model.pkl you downloaded from the repo you mentioned is an old and incompatible version.

To avoid this kind of problem, we should stick to best practices such as using a requirements.txt file defining exactly the versions using during development. The same versions could then be installed on production.

If you really need to use this model.pkl from the repo, I'd suggest creating a GitHub issue asking for a requirement.txt file. Alternatively, you could try different scikit-learn versions until you get it working, but this can be very cumbersome.

When RocksDB is used as the state backend for a Flink application, then the working copy of any key-partitioned state is stored in a local, embedded RocksDB instance in each task manager. Timers may kept there as well, or they may be on the heap. RocksDB keeps its state on the local disk; non-keyed state is always on the heap.

When a snapshot is taken (i.e., during checkpointing or when taking a savepoint), the state stored in RocksDB is (asynchronously) copied to the snapshot storage (which should be a distributed filesystem).

In your application, when you call flagState.update(true), for example, that ends up here, in RocksDBValueState.java, which uses this code to write to RocksDB:

After searching on stackoverflow I found a different but similar problem a person had. This is the link to the problem: Capture mouseover in d3 on a leaflet map

So to fix either his problem or my problem what we had to do is add an attribute to the circles called "pointer-events" and set it to visible. This is the result:

Unfortunately Bond is not currently supported by Stream Analytics. You will need to convert your Bond messages into one of JSON, Avro, or CSV outside of Stream Analytics.

No, it can't be linear. I don't have your complete code, but try

One solution is to first split by whitespace, then convert to datetime objects, then extract components directly.

Accuracy on an autoencoder has little meaning, especially on a fraud detection algorithm. What I mean by this is that accuracy is not well defined on regression tasks. For example is it accurate to say that 0.1 is the same as 0.11. For the keras algorithm it is not. If you want to see how well your algorithm replicates the data I would suggest looking at the MSE or at the data itself. Many autoencoder use MSE as their loss function.

The metric you should be monitoring is the training loss on good examples vs the validation loss on fraudulent examples. There you can easily see if you can fit your real examples more closely than the fraudulent ones and how well your algorithm performs in practice.

Another design choice I would not make is relu in an autoencoder. ReLU works well with deeper model because of its simplicity and effectiveness in combating vanishing/exploding gradients. However, both of this concerns are non-factors in autoencoder and the loss of data hurts in an autoencoder. I would suggest using tanh as your intermediate activation function.