the whole purpose of transaction detail is to understand the purchasing pattern of customer and product which were sold by the customer as well. user history file tells us on which day, an hour of the day, and day of the month, the user has purchased a particular product, as well as the relationship between the product, department, and aisles. i am going to explain the former one which is to create a connection between user and product as it tells us a lot of stories about both of them. from the graph, it is clear that users do shopping mostly in the first week and shopping gradually decreases from 8 days to till to the 29 days of the month.
from the graph, it is clear between the range of 1 to 45, products are added to the cart by users and reorder. the top 2 departments are found to be the department from which product has been ordered more. in training as well as test dataset, products have been associates with users on which we have to train and test the model. in the future, i would like to use the relation of user with reorder probability and merge it with the above and find the maximum mean f1 score. the highest f1 score on the kaggle leaderboard is 0.407. i would love to dive down and find more solutions in the future.
in 2015, i won 2nd place in the kdd cup 2015 challenge, where the goal of the challenge was to predict the probability that a student would drop out of a course in 10 days. second, this competition seemed to have clean data, and i thought that there might be a lot of room for feature engineering. the problem is a little different from the general recommendation problem, where we often face a cold start issue of making predictions for new users and new items that weâve never seen before. common sense tells us that an item purchased many times in the past has a high probability of being reordered.
so this feature tells us how ready the user is to repurchase the item. this will happen if we use a threshold between 0.3 and 0.9. similarly, for the order in the second row, our optimal choice is to predict that items a and b will both be reordered. this will happen is long as the threshold is less than 0.2 (the probability that item b will be reordered). so the most important thing is to participate in the delusion that youâll get a better result if you try!
in this competition, instacart is challenging the kaggle community to use this anonymized data on customer orders over time to predict which previously the dataset is anonymized and contains a sample of over 3 million grocery orders which products will an instacart consumer purchase again?, instacart market basket analysis, instacart market basket analysis, instacart market basket analysis in python, instacart market basket analysis solution, grocery dataset for market basket analysis.
our task is to predict which items will be reordered on the next order. the dataset consists of information about 3.4 million grocery orders, distributed across in instacart competition, the user purchase history which is a complete temporal based data of each customer has been provided and the problem our recent instacart market basket analysis competition challenged kagglers to predict which grocery products an instacart consumer will, instacart dataset download, instacart dataset 2020, instacart-market-basket-analysis github, instacart dataset analysis.
When you try to get related information on kaggle instacart, you may look for related areas. instacart market basket analysis, instacart market basket analysis in python, instacart market basket analysis solution, grocery dataset for market basket analysis, instacart dataset download, instacart dataset 2020, instacart-market-basket-analysis github, instacart dataset analysis.