Association A priori, Clustering K-means, Classification Decision Tree train and deploy sib6nf
This workflow contains three different methods applied to a single dataset named "Netflix Titles": association (using the Apriori algorithm), clustering (using K-means), and classification (using Decision Tree).
Each method serves a different purpose. The association method is used for identifying relationships between different attributes within the data, the clustering method groups similar items together, and the classification method predicts the category of new data entries. This data is intended for film companies looking to produce new films or individuals seeking to find similar movies to those they have already watched.
This dataset was provided by the Academy and analysis and workflow were created by Group 5 Kelompok 7 SIB Batch 6 at the Nurul Fikri Academy ACDS
Members :
1.Aris Saparudin
2.Leohafs Sri Indrapura R Zain
3.Fahri Prastyo
4.Rika Karmila
5.Mufidah Anjani Zarifah
Nodes
- Column Filter3 ×
- Missing Value3 ×
- Color Manager2 ×
- Association Rule Learner1 ×
- CSV Reader1 ×
- Cluster Assigner1 ×
- Create Collection Column1 ×
- Decision Tree Learner1 ×
- Decision Tree Predictor1 ×
- Duplicate Row Filter1 ×
- GroupBy1 ×
- Partitioning1 ×
- Scatter Plot1 ×
- Scorer1 ×
- Sorter1 ×
- String Manipulation1 ×
- String To Number1 ×
- Table View (JavaScript)1 ×
- k-Means1 ×