cot_reports is a Python library for fetching the Commitments of Trader reports of the Commodity Futures Trading Commission (CFTC). The following COT reports are supported: Legacy Futures-only, Legacy Futures-and-Options Combined, Supplemental Futures-and-Options Combined, Disaggregated Futures-only, Disaggregated Futures-and-Options Combined, Tr…

Translating text attributes (like name, address, phone number) into quantifiable numerical representations Training ML models to determine if these numerical labels form a match Scoring the confidence of each match

Perform fine-grained forecasting at the store-item level in an efficient manner, leveraging the distributed computational power of the Databricks Data Intelligence Platform.

Graduated cylindrical shell CME model in Python

Connect the impact of marketing and your ad spend to sales. Efficiently pinpoint the impact of various revenue-generating marketing activities to understand what works best. Focus on the best-performing channels to optimize media mix and drive revenue.

Create advanced customer segments to drive better purchasing predictions based on behaviors. Using sales data, campaigns and promotions systems, this solution helps derive a number of features that capture the behavior of various households. Build useful customer clusters to target with different promos and offers.

From display to video, the value of an impression can only be realized if an ad is viewed by a user. Therefore, when using programmatic advertising to buy inventory, it’s important to take viewability into account. In this Solution Accelerator, learn how to predict ad viewability to optimize your real-time bidding strategy.

CME Arrival Time Prediction Using Convolutional Neural Network

Build a lakehouse for all your gamer data and use natural language processing techniques to flag questionable comments for moderation.

Build a wide-and-deep recommender with collaborative filters that takes advantage of patterns of repeat purchases to suggest both previously purchased and related products.

Analyze the CME grain options markets in python

Use futures symbols to search and get their contract specs From CME website

Repository for Pachter Lab Biophysics

Get started with our Solution Accelerator for Propensity Scoring to build effective propensity scoring pipelines that: Enable the persistence, discovery and sharing of features across various model training exercises Quickly generate models by leveraging industry best practices Track and analyze the various model iterations generated

Survival analysis is a collection of statistical methods used to examine and predict the time until an event of interest occurs. In this Solution Accelerator, learn how to use different survival analysis techniques for predicting churn and calculating lifetime value.

This repository provides a python code to infer morphological parameters of Coronal Mass Ejection using Cone model given by Xie et al.,2004.

Professional-grade options pricing and analytics platform with real-time market data, advanced visualization, and multiple option pricing models.