facebookexperimental / Robyn
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.
Summary
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| Main Code: 394 LOC (6 files) = JS (80%) + CSS (19%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 17 LOC (1); Other: 225 LOC (6); |
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| Duplication: 3% | |||
| File Size: 0% long (>1000 LOC), 100% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 100% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 100% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (3 components) | ||
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1 year, 6 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
Latest commit date: 2022-03-31
Reference analysis date: 2022-04-01
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37
commits
(30 days)
6
contributors
(30 days) |
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