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KNIME_​project2

OUTLIERS

DATA Analysis 3

DATA TRANSFORMATION 4

DATA CLEANING 2

EDA 1

Visualisation 5

Numeric Outliers
Handling missing values(for:age,:mean , work province and country:unknown)
Missing Value
GENDER ↔ BP_SUM(bonus)
Scatter Plot
Math Formula
Column Auto Type Cast
age>100
Row Filter
Segmentation of age
Binner
Table View
avg_discount<1
Row Filter
Days_Since_Last_Flight
Date&Time Difference
Min-Max
Normalizer
String to Date&Time
columnn AGE_BIN5adult,junior, senior)
Rule Engine
delete column city , province
Column Filter
Statistics View
Missing Value
FFP_TIER ↔ FLIGHT_COUNT
GroupBy
Table View
Spelling , add Fwork_province (majiscule)
String Manipulation
Rule Engine
Spelling , add Fwork_city (majiscule)
String Manipulation
Bar Chart
Number to String
Age_bin ↔ FLIGHT_COUNT
GroupBy
Bar Chart
Bar Chart
FFP_TIER ↔ Mean(SEG_KM_SUM)
GroupBy
Dataset is clean and ready now !
Table View
Number to String
Age_bin <->occurence count
Bar Chart
Number to String
Statistics View
Number to String
AGE ↔ FLIGHT_COUNT
GroupBy
remove column if 90%of it is empty
Missing Value Column Filter
Bar Chart
CSV Reader
Scatter Plot
Add column Fwork _province . replace"." by unknown
String Manipulation
Add column Fwork _city . replace"." by unknown
String Manipulation
Gender <-> =Flight-count
Box Plot

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: LENOVO
Created at: 2026-02-05
On NodePit since: 2026-02-06
Last update: 2026-02-11
Created with KNIME version: v5.9.0

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