Capture Internet Dynamics using Prediction (kNN)

In this project, I have given a dataset of trace-route for 10 days each hour from planet lab. I have created a database in Neo4j (NoSQL data base). Due to memory, node and relationship limitation, I put the data partially. You can see the images from folder neo4j-database-images.

Then I have done visualization to see the parameter/attributes relationship among themselves. Then I have processed the given data, so that I can apply the kNN algorithm. At the end I have applied kNN algorithm.

tree of the repository ├── Code │   ├── kNN │   │   ├── euclideanDistance.py │   │   ├── kNN.py │   │   └── main.py │   ├── preprocessing │   │   ├── ip_merge.py │   │   ├── Ip-processing.py │   │   ├── merge.py │   │   └── PCA.py │   └── visualization │   ├── visual_2.py │   └── visual.py ├── NEO4J │   └── test_csv.cypher ├── neo4j-database-images │   ├── AS-has-ip-IP-info-Source-and-Edge.jpg │   ├── AS-Ip-relation-25.jpg │   ├── Edge-limit-25.jpg │   ├── neo4j_Trace_route_database.jpg │   └── source-25.jpg └── results ├── predicted_stats │   ├── result.jpeg │   └── start-stop-error.jpeg └── visualization ├── AS-id-att.jpeg ├── AS-IP.png ├── geolocation-IP.png ├── src-des-time.png └── time_visualization.png