# 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
• Welcome to the site. How big is your dataset for evaluating accuracy (for new product)? Beyond TF-IDF of product, can you think of incorporating seller's past history (example: number of sales s/he completes in a month) to capture the average size of the business? Posting few examples from the data here would help to give more detailed answers. Apr 12, 2021 at 6:06
• I have updated the important information that I can use regarding seller's information. Apr 12, 2021 at 6:28
• How did you label your data ? How did you arrive at the 70% accuracy ? Apr 12, 2021 at 6:57
• For TF-IDF, I simply got the relevant products and predicted the supplier with the most number of transactions for those relevant products. For Random Forest Algorithm, I used one-hot encoding for predictors and I labeled sellers with their names only. I trained data for 11 months and I predicted for the 12th month. Then, I compared the predictions with actual data of the 12th month to calculate accuracy. Apr 12, 2021 at 7:37