Predicting Spending Interest Report

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Data Summary:

Data	Value
Problem Type	Multiclass Classification
Number of rows on training data	2000
Training - to - Test Ratio	80 to 20
Feature count	15
Number of categorical features	5
Number of Numeric features	10
Target Variable	Target-MCC1
Target Variable Type	Categorical
Number of classes in Target	10
Values of classes in Target	4111, 4814, 5411, 5621, 5691, 5812, 5983,7032, 5999, 7832

MCCs Lookup - Definition

Merchant category code	Description
7832	Motion Picture Theaters
5411	Grocery Stores
5812	Eating places and Restaurant
7032	Sporting and Recreational Camps
5999	Miscellaneous and Specialty Retail Stores
4111	Transportation
5691	Men’s and Women’s Clothing Stores
5983	Fuel Oil, Liquefied Petroleum
4814	Telecommunication services
5621	Women’s Ready-to-Wear Stores

Sample Top 5 Rows

AgeGroup	Marital Status	Day	Gender	City	Avg-7832	Avg-5411	Avg-5812	Avg-7032	Avg-5983	Avg-4111	Avg-5999	Avg-5691	Avg-4814	Avg-5621	Target-MCC1
20	Single	Weekday	F	KHI	10	25	20	10	5	5	5	5	15	0	5411
20	Single	Weekday	F	KHI	0	10	0	65	25	0	0	0	0	0	7032
30	Single	Weekday	M	KHI	10	5	0	0	32	0	15	0	0	38	5621
40	Single	Weekday	F	ISL	0	22	10	3	3	5	7	2	0	48	5621
30	Married	Weekday	F	ISL	10	25	20	10	5	5	5	5	15	0	5411

Cardinality:

Refers to the number of possible values that a categorical feature can assume:

Feature	Number of Unique Values	Unique Values
AgeGroup	3	20s, 30s, 40s
City	3	KHI, LHR, ISL
Day	2	Weekday, Weekend
Gender	2	Male, Female

ML Algorithm

Algorithm	Value
Problem Type	Multiclass Classification
Name	Random Forest

Hyperparameter	Values
n_estimators	100
random_state	42
max_depth	none

Model Accuracy

Metric	Value
Model Accuracy	0.93

Feature Importance

Feature	Importance
AgeGroup	0.007335352846323242
MaritalStatus	0.003780795258948365
Day	0.006009442644614128
Gender	0.004859433183447916
City	0.006545369156478366
Avg-7832	0.46691761668599463
Avg-5411	0.03854568023937666
Avg-5812	0.01321235913167839
Avg-7032	0.2247785109812169
Avg-5983	0.01376515795764316
Avg-4111	0.10336592425084284
Avg-5999	0.03103362660596684
Avg-5691	0.016577535325028935
Avg-4814	0.013617798502985055
Avg-5621	0.04965539722945451

Classification Report:

          precision    recall  f1-score   support

    4111       0.71      0.55      0.62        22
    4814       1.00      1.00      1.00         1
    5411       0.92      0.95      0.93        58
    5621       0.98      1.00      0.99        89
    5691       0.88      0.95      0.91        56
    5812       0.92      0.85      0.88        40
    5983       1.00      1.00      1.00         7
    7032       0.98      0.96      0.97        55
    7832       0.93      0.94      0.94        72

accuracy                           0.93       400
macro avg       0.92      0.91     0.92       400
weighted avg    0.93      0.93     0.93       400

Definitions

Precision

It is the proportion of true positive predictions out of all the positive predictions. It measures the accuracy of positive predictions. It Answers the following question: what proportion of predicted Positives is truly Positive?

Precision = TP/(TP+FP)

Recall:

It is the proportion of true positive predictions out of all the actual positive cases. It measures the completeness of positive predictions. what proportion of actual Positives is correctly classified? recall is

Recall = TP/(TP+FN)

Accuracy:

what proportion of photos — both Positive and Negative — were correctly classified? proportion of correct predictions out of all the predictions made by the model on the test data accuracy is

Accuracy = (TP+TN)/(TP+TN+FP+FN)

F1-score:

It is the harmonic mean of precision and recall. It balances both the metrics and gives an overall measure of the model's performance.

F1-score = 2 × (precision × recall)/(precision + recall)

Support:

It is the number of actual occurrences of the class in the dataset.

The report includes these metrics for each class in the target variable. In this case, there are ten classes represented by the numbers 4111, 4814, 5411, 5621, 5691, 5812, 5983, 7032, 5999, 7832.

• The macro average and weighted average of precision, recall, and F1-score are also provided.
• Macro average is the unweighted average of each metric across all the classes.
• Weighted average considers the number of samples in each class.