Prediction Algorithm for Data with high Randomness I have data for the orders of the previous year containing the product and the seller who sold the product. I have an information product, product category, seller, delivery address price etc. Database size is more than 100,000 rows. Now, I have to suggest a seller for a totally new product based on the data I have. I tried using TF-IDF to find similar products of the same category to suggest the sellers and I got an accuracy of 70%. Then, I tried a random forest algorithm and sadly I got an accuracy of just 40%. I am unable to find a suitable approach for my use case. How can I approach this problem statement?
The Product and Seller Mapping table is like this




productId
sellerId
price
purchase Date
deliveryAddressId




1
4
100
9-01-2012
4


2
12
400
1-08-2020
4


1
1
123
4-09-2020
1


2
3
450
24-12-2020
1


3
4
150
14-05-2020
2


5
3
430
12-02-2020
2




Product has the following information




productId
name
categoryId




1
AC
1


2
TV
1


3
Food
2


4
Toy
3


5
Car
3


6
Book
4




Seller has the following information




sellerId
sellerName
totalTransactions




1
A
81


2
B
111


3
C
200


4
D
42
You can use seller mean buying price, std buying price, max buying price, min buying price, median buying price PLUS include recently user buying power to suggest the totally new product to the user given the current data that's best I can recommend although extensive data can lead to better suggestions.