🦔 PostHog is an all-in-one developer platform for building successful products. We offer product analytics, web analytics, session replay, error tracking, feature flags, experimentation, surveys, data warehouse, a CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack.

Modern, privacy-friendly, and detailed web analytics that works without cookies or JS.

Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.

The AutoGPT Google Analytics Plugin is a software tool that allows you to connect Google Analytics to Auto-GPT.

Ballcone is a fast and lightweight server-side Web analytics solution.

Django application to proxy requests and send server-side events to Plausible Analytics.

🗃️ A toy analytics system for the web with a focus on user privacy.

Projekt för att kontinuerligt övervaka en webbplats mätbara kvalitetsfaktorer.

Python package to send hits to Google Analytics through its Measurement Protocol API.

A super-fast user agent string parser

Automates the collection, transformation, and presentation of web analytics data from Google Analytics 4 and Google Search Console into Google Sheets for streamlined reporting and analysis.

Add Matomo Web Analytics JavaScript tracking code to Datasette

A modern-style implementation of Baidu Analytics (Tongji) in Python.

The main aim of the project is to scrap reviews from a website for American cuisine restaurants in NYC. Then from the scrapped reviews, it would extract the context and calculate the accuracy for each context. (Contexts considered are: Whom, When, Where, and Occasion) - 'Whom' denotes, with whom the user went to the restaurant (example: friends,…

The main aim of the project is to scrap reviews from a website for American cuisine restaurants in NYC. Then from the scrapped reviews, it would extract the context and calculate the accuracy for each context. (Contexts considered are: Whom, When, Where, and Occasion) - 'Whom' denotes, with whom the user went to the restaurant (example: friends,…

For a detailed project breakdown, plese read the @Readme

a recon webapp built with python's Flask designed to gather data about currently used large-scale web attacks and optionally attempt to stall bots.

This project leverages AI techniques including supervised and unsupervised machine learning, pattern mining, and statistical inference to analyze web browsing behavior. It identifies frequent navigation sequences and predicts user demographic profiles using simulated data. Complete with a GUI, logging, and export capabilities.