Kaggle | Instacart Market Basket Analysis🥕🥉
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My solution for the Instacart Market Basket Analysis competition hosted on Kaggle.
The dataset is an open-source dataset provided by Instacart (source)
This anonymized dataset contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of day the order was placed, and a relative measure of time between orders.
Below is the full data schema (source)
orders(3.4m rows, 206k users): *order_id: order identifier *user_id: customer identifier *eval_set: which evaluation set this order belongs in (seeSETdescribed below) *order_number: the order sequence number for this user (1 = first, n = nth) *order_dow: the day of the week the order was placed on *order_hour_of_day: the hour of the day the order was placed on *days_since_prior: days since the last order, capped at 30 (with NAs fororder_number= 1)products(50k rows): *product_id: product identifier *product_name: name of the product *aisle_id: foreign key *department_id: foreign keyaisles(134 rows): *aisle_id: aisle identifier *aisle: the name of the aisledeptartments(21 rows): *department_id: department identifier *department: the name of the departmentorder_products__SET(30m+ rows): *order_id: foreign key *product_id: foreign key *add_to_cart_order: order in which each product was added to cart *reordered: 1 if this product has been ordered by this user in the past, 0 otherwise
whereSETis one of the four following evaluation sets (eval_setinorders): *"prior": orders prior to that users most recent order (~3.2m orders) *"train": training data supplied to participants (~131k orders) *"test": test data reserved for machine learning competitions (~75k orders)
The task is to predict which products a user will reorder in their next order. The evaluation metric is the F1-score between the set of predicted products and the set of true products.
The task was reformulated as a binary prediction task: Given a user, a product, and the user's prior purchase history, predict whether or not the given product will be reordered in the user's next order. In short, the approach was to fit a variety of generative models to the prior data and use the internal representations from these models as features to second-level models.
The first-level models vary in their inputs, architectures, and objectives, resulting in a diverse set of representations. - Product RNN/CNN (code): a combined RNN and CNN trained to predict the probability that a user will order a product at each timestep. The RNN is a single-layer LSTM and the CNN is a 6-layer causal CNN with dilated convolutions. - Aisle RNN (code): an RNN similar to the first model, but trained at the aisle level (predict whether a user purchases any products from a given aisle at each timestep). - Department RNN (code): an RNN trained at the department level. - Product RNN mixture model (code): an RNN similar to the first model, but instead trained to maximize the likelihood of a bernoulli mixture model. - Order size RNN (code): an RNN trained to predict the next order size, minimizing RMSE. - Order size RNN mixture model (code): an RNN trained to predict the next order size, maximizing the likelihood of a gaussian mixture model. - Skip-Gram with Negative Sampling (SGNS) (code): SGNS trained on sequences of ordered products. - Non-Negative Matrix Factorization (NNMF) (code): NNMF trained on a matrix of user-product order counts.
The final reorder probabilities are a weighted average of the outputs from the second-level models. The final basket is chosen by using these probabilities and choosing the product subset with maximum expected F1-score.
64 GB RAM and 12 GB GPU (recommended), Python 2.7
Python packages: - lightgbm==2.0.4 - numpy==1.13.1 - pandas==0.19.2 - scikit-learn==0.18.1 - tensorflow==1.3.0