Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.

FIX order manager client for fix order routing in C++ using QuickFIX engine can be used for Trading Technologies (TT) or CQG and others

A minimalist, low-latency, HFT CME MDP 3.0 C++ market data feed handler implementing all required features

Repository for Pachter Lab Biophysics

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…

Risk tools for commodities trading and finance

Project for Physical Methods of Biology based on Kim JK, Tyson JJ (2020) Misuse of the Michaelis–Menten rate law for protein interaction networks and its remedy. PLoS Comput Biol 16(10): e1008258. https://doi.org/10.1371/journal. pcbi.1008258.

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

Crackmapexec 6.0.0 Bane Edition Re-upload

Perl module to create configuration editor with semantic validation

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.

Identifying Campaign Effectiveness For Forecasting Foot Traffic

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.

Preempt churn with the Databricks Solution Accelerator for predicting subscriber attrition. Learn how to analyze behavioral data to identify subscribers with an increased risk of cancellation. Then use machine learning to quantify the likelihood to churn as well as indicate which factors explain that risk.

Increase viewer retention through data-driven engagement strategies: analyze both streaming and batch data sets to ensure a performant streaming content experience that drives engagement and loyalty.

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.

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

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

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

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.