The dataset contains data of 4 different diseases and the cost patients from 10 different states incurred during treatment in July 2023. The first step is installing the necessary Python libraries and the dataset:

PatientID TreatmentDate AmountSpent in $ DiseaseType TreatmentNumber \ 0 4600 2023-07-17 28226 Heart disease 562915

1 6302 2023-07-17 979 Hypertension 563003

2 2456 2023-07-17 947 Hypertension 878136

3 5602 2023-07-17 703 Diabetes 120090

4 1811 2023-07-17 27380 Lung Cancer 140526

0 California

1 Texas

2 Kentucky

3 Delaware

4 Alaska

Calculating RFM Values The next step is to calculate the Recency, Frequency, and Monetary values of the patients. from datetime import datetime

PatientID TreatmentDate AmountSpent in $ DiseaseType TreatmentNumber \ 0 4600 2023-07-17 28226 Heart disease 562915

1 6302 2023-07-17 979 Hypertension 563003

2 2456 2023-07-17 947 Hypertension 878136

3 5602 2023-07-17 703 Diabetes 120090

4 1811 2023-07-17 27380 Lung Cancer 140526

0 California 92 days 1 28226 1 Texas 92 days 1 979 2 Kentucky 92 days 1 947

3 Delaware 92 days 1 703

4 Alaska 92 days 1 27380

Calculating RFM Scores This step involves calculating the recency, frequency, and monetary scores

PatientID TreatmentDate AmountSpent in $ DiseaseType TreatmentNumber \ 0 4600 2023-07-17 28226 Heart disease 562915

1 6302 2023-07-17 979 Hypertension 563003

2 2456 2023-07-17 947 Hypertension 878136

3 5602 2023-07-17 703 Diabetes 120090

4 1811 2023-07-17 27380 Lung Cancer 140526

0 California 92 days 1 28226 1 1 1 Texas 92 days 1 979 1 1 2 Kentucky 92 days 1 947 1 1 3 Delaware 92 days 1 703 1 1 4 Alaska 92 days 1 27380 1 1 MonetaryScore RFM_Score Value Segment

0 2 4 Low-Value

1 1 3 Low-Value

2 1 3 Low-Value

3 1 3 Low-Value

4 2 4 Low-Value

Combining RFM Scores

PatientID TreatmentDate AmountSpent in $ TreatmentNumber

0 4600 2023-07-17 28226 562915

1 6302 2023-07-17 979 563003

2 2456 2023-07-17 947 878136

3 5602 2023-07-17 703 120090

4 1811 2023-07-17 27380 140526

0 92 days 1 28226 1 1 1 92 days 1 979 1 1 2 92 days 1 947 1 1 3 92 days 1 703 1 1 4 92 days 1 27380 1 1 MonetaryScore RFM_Score 0 2 4

1 1 3

2 1 3

3 1 3

4 2 4

Lastly, the RFM_score column combines the individual scores for recency, frequency, and monetary value into a single RFM score. This score can be used to segment customers and gain insights into their behavior and preferences.