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Classification Klasifikasi SVM Dataset Bank sib6nf

Indonesia

Workflow ini di buat untuk mengetahui seberapa banyak yang berlangganan deposito atau tidak dengan tujuan memberikan tingkat pemasaran yang sesuai dengan pelanggan bank.

Workflow ini dibuat untuk pemenuhan tugas final project SIB-6 Academy Codeles Data Science (ACDS)

Dataset : bank-full.csv

Group-5 Kelompok-2
Ketua : Muzaki Abdullah Irsyad, Ira Kusuma Wardani, Ira Kusuma Wardani, Muhammad Azmi, Wahyu Akhir Ramadhan, Fatih

This workflow was created to find out how many people subscribed to deposits or not with the aim of providing an appropriate level of marketing to bank customers.

This workflow was created to fulfill the final assignment of the SIB-6 Academy Codeles Data Science (ACDS) project

Dataset : bank-full.csv

Group-5 Group-2
Chairman: Muzaki Abdullah Irsyad, Ira Kusuma Wardani, Ira Kusuma Wardani, Muhammad Azmi, Wahyu Akhir Ramadhan, Fatih

Classification of the iris dataset using SVM Train SupportVector MachinePerform classificationon test data80% training20% testinto [0,1]Memasukkan dataMemilih data yang diperlukanHasil dari prosesMemasukkan dataMenghilangkandata kosongHail korelasi SVM Learner SVM Predictor Partitioning Normalizer Normalizer (Apply) CSV Reader Column Filter Scorer (JavaScript) CSV Reader Missing Value Rank Correlation Classification of the iris dataset using SVM Train SupportVector MachinePerform classificationon test data80% training20% testinto [0,1]Memasukkan dataMemilih data yang diperlukanHasil dari prosesMemasukkan dataMenghilangkandata kosongHail korelasiSVM Learner SVM Predictor Partitioning Normalizer Normalizer (Apply) CSV Reader Column Filter Scorer (JavaScript) CSV Reader Missing Value Rank Correlation

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Emilio Silvestri
Created at: 2020-02-12
On NodePit since: 2024-06-28
Last update: 2026-01-27
Created with KNIME version: v5.2.1
Tags: TheGuideBookSVMclassificationSupport Vector MachineGIDS

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