retentioneering-tools
sweetviz
| retentioneering-tools | sweetviz | |
|---|---|---|
| 1 | 1 | |
| 763 | 2,837 | |
| 1.2% | - | |
| 5.9 | 6.7 | |
| 5 months ago | 5 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
retentioneering-tools
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My Favorite Off-the-Shelf Data Science Repos, What Are Yours?
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.
sweetviz
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Automated Data Profiling and Attribute Clustering using unsupervised ML techniques
Take a look at this package which computes associations between variables and other viz and can infer some types https://github.com/fbdesignpro/sweetviz
What are some alternatives?
metarank - A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
dataprep - Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
dbt-fal - do more with dbt. dbt-fal helps you run Python alongside dbt, so you can send Slack alerts, detect anomalies and build machine learning models.
popmon - Monitor the stability of a Pandas or Spark dataframe ⚙︎
Contactless-Attendance-System - ✨ A Contactless Attendance System where your face is identified for Attendance.
dtale-desktop - Build a data visualization dashboard with simple snippets of python code
mlgauge - A simple library to benchmark the performance of machine learning methods across different datasets.
lux - 👾 Fast and simple video download library and CLI tool written in Go