Skip to main content

One post tagged with "regression"

View All Tags

· 2 min read
Sparsh Agarwal

/img/content-blog-raw-blog-predicting-electronics-resale-price-untitled.png

Objective

Predict the resale price based on brand, part id and purchase quantity

Milestones

  • Data analysis and discovery - What is the acceptable variance the model needs to meet in terms of similar part number and quantity?
  • Model research and validation - Does the model meet the variance requirement? (Variance of the model should meet or be below the variance of the sales history)
  • Model deployment - Traffic will increase 10 fold. So, model needs to be containerized or dockerized
  • Training - Model needs to be trainable on new sales data. Methodology to accept or reject the variance of the newly trained model documented.

Deliverables

  1. Data Analysis and Discovery (identify target variance for pricing model in terms of similar part numbers and quantities). Analysis should be done on the 12 following quantity ranges: 1-4, 5-9, 10-24, 25-49, 50-99, 100-249, 250-499, 500-999, 1000-2499, 2500-4999, 5000-9999, 10000+.

  2. ModelA Training (Resale Value Estimation [$] (Brand+PartNo.+Quantity)

  3. ModelA Validation (variance analysis and comparison with sales history variance in terms of similar part numbers and quantities)

  4. ModelA Containerization

  5. ModelA re-training based on new sales data

  6. ScriptA to calculate variance for new sales data (feedback for training results)

  7. Documentation for re-training

  8. ModelA deployment and API

Modeling Approach

Framework​

  • Fully connected regression neural network
  • NLP feature extraction from part id
  • Batch generator to feed large data in batches
  • Hyperparameter tuning to find the best model fit

List of Variables​

  • 2 years of sales history
  • PRC
  • PARTNO
  • ORDER_NUMBER
  • ORIG_ORDER_QTY
  • UNIT_COST
  • UNIT_REASLE
  • UOM (UNIT OF MEASUREMENT)

Bucket of Ideas

  1. Increase n-gram range; e.g. in part_id ABC-123-23, these are 4-grams: ABC-, BC-1, C-12, -123, 123-, 23-2, 3-23; Idea is to see if increasing this range further will increase the model's performance
  2. Employ Char-level LSTM to capture sequence information; e.g. in same part_id ABC-123-23, currently we are not maintaining sequence of grams, we don't know if 3-23 is coming at first or last; here, the idea is to see if lstm model can be employed to capture this sequence information to improve model's performance
  3. New Loss function - including cost based loss