My Favorite Off-the-Shelf Data Science Repos, What Are Yours?

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • metarank

    A low code Machine Learning service that personalizes articles, listings, search results, recommendations to boost user engagement. A friendly Learn-to-Rank engine

    Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?

    Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)

    Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.

    User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)

    Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.

    Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)

    Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.

  • retentioneering-tools

    Retentioneering: product analytics, data-driven customer journey map optimization, marketing analytics, web analytics, transaction analytics, graph visualization, and behavioral segmentation with customer segments in Python. Opensource analytics, predictive analytics over clickstream, sentiment analysis, AB tests, machine learning, and Monte Carlo Markov Chain simulations, extending Pandas, Networkx and sklearn.

    Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?

    Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)

    Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.

    User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)

    Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.

    Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)

    Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.

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  • 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, 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 (by facebookexperimental)

    Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?

    Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)

    Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.

    User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)

    Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.

    Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)

    Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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