Problem Statement
Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph).
Data Overview
Taken data from facebook's recruting challenge on Kaggle.
Data contains two columns source and destination eac edge in graph.
- Data Columns (total 2 columns):
- source_node int64
- destination_node int64
Mapping the problem into supervised learning problem
Generated training samples of good and bad links from given directed graph and for each link got some features like no of followers, is he followed back, page rank, katz score, adar index, some svd fetures of adj matrix, some weight features etc. and trained ml model based on these features to predict link.
Business Objectives and Constraints
- No low-latency requirement
- Probability of prediction is useful to recommend ighest probability links
Performance metric for supervised learning
- Both precision and recall is important so F1 score is good choice
- Confusion matrix
Posing a Problem as a Classification Problem
Click Here To Check Exploratory Data Analysis - 1_FB_EDA_
Click Here To Check Work on The Features (Extraction) - 2_FB_Featurization_
Click Here To Check Work on Models (ML) - 3_FB_Models_
| Models | Train F1 Score | TestF1 Score |
|---|---|---|
| Random Forest | 0.96 | 0.92 |
| XG Boost | 1.0 | 0.92 |