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- access to all Manning books, including MEAPs!
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Look inside
You’ve set your sights on a collection of prime off-the-market New York City properties whose value is likely to double in the next couple of years. With your excellent sales skills, you’re confident you can get the owners to sell at a fair price, if you can determine who the owners are. You have a knowledge graph—built from tax records, property deeds, and permits—that identifies entities associated with the properties. These entities might not be the true owners, but identifying them could help you determine who the true owners are.
Your task is to transform the knowledge graph into a representation that can be processed by a machine learning model later. Using simple graph and NLP techniques, including node2vec—one of the most influential algorithms in the graph community—you’ll improve the quality of the data by removing the noise from the knowledge graph. You’ll convert the nodes in your graph into embeddings, analyze how well your embeddings represent the underlying knowledge graph, and develop insights on tuning the node2vec hyperparameters. When you’re done, you’ll have learned techniques for analyzing and visualizing embeddings and associating them to the original graph, helping you determine who the “hidden” owners are.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
prerequisites
This liveProject is for data scientists who have a background in graph theory and machine learning and are interested in applying these techniques to a knowledge graph. To begin these liveProjects, you’ll need to be familiar with the following:
TOOLS- Intermediate Python 3.x skills
- Ability to create lambda functions and list comprehensions
- Intermediate Jupyter Notebook skills
- Ability to execute and debug cells
- Experience in visualizing pandas output
- Intermediate Neo4j skills
- Familiarity with the Cypher query language
- Intermediate pandas
- Ability to read, write, and query data from csv files
- Basic graph theory
- Familiar with the concept of nodes and edges
- Intermediate machine learning
- Familiar with the concept of embeddings and the k-nearest neighbor algorithm
you will learn
In this liveProject, you’ll learn techniques for analyzing and visualizing graph embeddings and associating them back to the original graph, and you’ll understand how different schemas can impact the knowledge representation.
- Leverage a graph method (weakly connected components) and NLP to clean a noisy graph
- Apply the node2vec model to a graph
- Analyze a graph embedding space using the k-nearest neighbors algorithm
- Analyze graph data using Graph Data Science (GDS) libraries
- View graph embeddings in a 2d space using the t-SNE method
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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Find Hidden Connections with Graphs project for free
Find Hidden Connections with Graphs project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
![]()
Find Hidden Connections with Graphs project for free
