I have an problem where the dataset consists of:

• 400k observations
• 40k classes (mutually exclusive)

60% of the observations belong to the top 4k classes.

The problem is about predicting what is the supplier of a bank transaction (from which supplier/shop a purchase was made) based on the description of the transaction sent by the bank.

As you can understand there are hundreds of thousands if not millions of suppliers in a country hence the so big number of (mutually exclusive) classes.

The dataset has only 4 datapoints:

• Transaction id
• Buyer id
• Bank description
• Supplier id/name

Each description consists of only few "technical" words; for example: "Payment EU Vodafone 04/11/21".

About 60%-70& of the descriptions contain the supplier name within them; however in some cases it could be a different form (eg VDFN instead of Vodafone).

How would you solve this problem?

Is ML, DL or a rule based way the best way to go for it?

I have an problem where the dataset consists of:

• 400k observations
• 40k classes (mutually exclusive)

60% of the observations belong to the top 4k classes.

The problem is about predicting what is the supplier of a bank transaction (from which supplier/shop a purchase was made) based on the description of the transaction sent by the bank.

As you can understand there are hundreds of thousands if not millions of suppliers in a country hence the so big number of (mutually exclusive) classes.

The dataset has only 4 datapoints:

• Transaction id (eg 83883)
• Buyer id (eg 33)
• Bank description (eg "Payment EU Vodafone 04/11/21", " VDFN payment")
• Supplier id/name (eg VODAFONE)

Each description consists of only few "technical" words; for example: "Payment EU Vodafone 04/11/21".

About 60%-70& of the descriptions contain the supplier name within them; however in some cases it could be a different form (eg VDFN instead of Vodafone).

How would you solve this problem?

Is ML, DL, general DS (eg similarity metrics) or a rule based way the best way to go for it?

I have an problem where the dataset consists of:

• 400k observations
• 40k classes (mutually exclusive)

The problem is about predicting what is the supplier of a bank transaction (from which supplier/shop a purchase was made) based on the description of the transaction sent by the bank.

As you can understand there are hundreds of thousands if not millions of suppliers in a country hence the so big number of (mutually exclusive) classes.

Each description consists of few words; for example: "Payment EU Vodafone 04/11/21".

For this transaction there will be a supplier id attached to it; for example "4792847" (the hypothetical business id of Vodafone).

About 60%-70& of the descriptions contain the supplier name within them; however in some cases it could be a different form (eg VDFN instead of Vodafone).

How would you solve this problem? Is ML, DL or a rule based way the best way to go for it?

I have an problem where the dataset consists of:

• 400k observations
• 40k classes (mutually exclusive)

60% of the observations belong to the top 4k classes.

The problem is about predicting what is the supplier of a bank transaction (from which supplier/shop a purchase was made) based on the description of the transaction sent by the bank.

As you can understand there are hundreds of thousands if not millions of suppliers in a country hence the so big number of (mutually exclusive) classes.

The dataset has only 4 datapoints:

• Transaction id
• Buyer id
• Bank description
• Supplier id/name

Each description consists of only few "technical" words; for example: "Payment EU Vodafone 04/11/21".

About 60%-70& of the descriptions contain the supplier name within them; however in some cases it could be a different form (eg VDFN instead of Vodafone).

How would you solve this problem?

Is ML, DL or a rule based way the best way to go for it?