Analyze a Trust Network you own this product

prerequisites
basic Python • basic pandas • basic scikit-learn • basic statistics • basic NetworkX • basics of machine learning: clustering
skills learned
pandas for data operations • scikit-learn for clustering • NetworkX to analyze and visualize network datasets
Emre Rencberoglu
1 week · 4-6 hours per week · INTERMEDIATE
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In this liveProject, you’ll turn your data science skills to analyzing an OTC network dataset scraped from bitcoin users in order to establish the most (and least!) trustworthy users. You’ll analyze a provided graph dataset, visualize it, generate features, and then create user clusters. You’ll start out by reading and examining the trust network dataset in Python, then create and interpret user clusters, and finally visualize the nodes and edges of the network dataset. This fast and engaging data science project will stretch your skills and build your knowledge of clustering.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Emre Rencberoglu
Emre Rencberoglu is a senior data scientist with over seven years of experience in machine learning, statistics, analytics, and data engineering. He developed numerous machine learning projects and built data pipelines from scratch using R, Python and Spark. Currently, he is leading a data science team of ten in one of the biggest e-commerce companies in the Europe, Middle East, and Africa region.

prerequisites

The liveProject is for data practitioners interested in exploring network analysis. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Basic Python
  • Basic pandas: Dataframes, basic feature transformations
  • Basic scikit-learn
  • Basic NetworkX
  • Basics of Jupyter Notebook
TECHNIQUES
  • Basics of statistics
  • Basics of machine learning: Clustering

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