weather and products sell relationship which one should be the target variable? Let say I have this dataset 

kor_cat is the categories of the product like(noodle,cookie....)
 and the kor_qty is the number of product selled 

I want to know the weather has a relation with the store's sale record

but I want to know which machine learning technique is fit here 
and I have the categori of the product and number of selled of that product.

How should I solve this problem with ML.
Apparently, you have a target variable (y) and a set of input variables (input variables (X). This seems like a supervised problem. A regression analysis can be a solution to implement.

Feature engineering needs to be completed before running a machine learning model. Such as text field columns can be encoded with Label Encoder and One Hot Encoder. 

Irrelevant input columns for analysis can be dropped. If the city variable is same all the way, meaning the all of the data is from a single city. Also N/A and null values need to be cleaned. 

In some models, normalization of the data can produce better results. 

As a Machine Learning model, starting from Linear Regression would be feasible. Later Random Forest Regressor, Lasso, Ridge Regression can be examined.