Supervised and Unsupervised Machine Learning using KNIME

I have fully build the model in KNIME. All I need is report doing the analysis for both supervised and unsupervised (feel free to change the model if its week but I surely build it correctly)

1) Supervised assignement:

a) Develop a supervised learning machine learning model.

b) Perform a thorough analysis of the dataset and the machine learning output.

NOTE: It appears that if you use the test and truth file, the Predictor note predicts correctly for most of the values. A file formatting error seems to be causing the Numeric Scorer to output very large, incorrect values.

Solution: Please use a table partitioner. Split the Train dataset into 70/30. Use the 30% of the data for the regression predictor. Do not use the test and truth file at all.

Submit outputs from the Numeric Scorer as we have done in prior studies.

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Report Structure

a) Descriptive Statistics (25 points)

b) General Analysis of the dataset, and any patterns you observe (25 points)

c) Model Scores (25 points)

d) Analysis of the model score (25 points)

2) Unsupervised assignement :

Here is the dataset to use for this assignment.

The guidance for this assignment is the same as the prior Supervised Learning assignment.

The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Variables of Dataset
Balance
Balance Frequency
Purchases
One-off Purchases
Installment Purchases
Cash Advance
Purchases Frequency
One-off Purchases Frequency
Purchases Installments Frequency
Cash Advance Frequency
Cash Advance TRX
Purchases TRX
Credit Limit
Payments
Minimum Payments
PRC Full payment
Tenure
Cluster

The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders over 6 months. The file is at a customer level with 18 behavioral variables.

WHAT IS MARKET SEGMENTATION?

In marketing, market segmentation is the process of dividing a broad consumer or business market, typically consisting of existing and potential customers, into subgroups based on shared characteristics.

Objective : This case requires developing a customer segmentation to give recommendations like saving plans, loans, wealth management, etc. on target customer groups.

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Be elaborate with your analysis.

Report Structure –

a) Descriptive Statistics (25 points)

b) Model Metrics (25 points)

c) General analysis of the dataset, patterns you observe (25 points)

d) Analysis of the model metrics (25 points)

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