Performance Analysis of Alternative Datasets for Credit Scoring
Implementation plan:
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Step 1: Initially, we load and collect the data from the Financial Risk Analysis Dataset.
Step 2: Then, we perform data augmentation using “Generative adversarial network with a gradient penalty-based multi-task learning model (GAN-GP-MTL)”.
Step 3: Next, we preprocess the data using “Stacking-based Noise detection and Multi-stage Backward Cloud Generator (MBCG)” methods.
Step 4: Next, we extract the features using “Opti-Filter XGBoost” integrated with “Filter based with APSO –XGBoost”.
Step 5: Next, we implement the “Distributed Proximal Gradient Descent with Homomorphic Encryption (HE-DPGD)” algorithm to preserve privacy.
Step 6: Next, we Train the data effectively using “Back Propagation Artificial Neural network (BP-ANN)”
Step 7: Next, we implement the “Pareto Front Stochastic Multi-Gradient (PF-SMG) algorithm to maintain balance between accuracy and fairness.
Step 8: Finally, we plot performance for the following
8.1: Number of epochs Vs. Accuracy (%)
8.2: Number of epochs vs. Precision (%)
8.3: Number of epochs vs. Recall (%)
8.4: Number of epochs Vs. F1- score (%)
8.5: Number of epochs vs. G-Mean
8.6: Number of epochs vs. AUC
Software Requirement:
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1. Development Tool: Python – 3.11.4 or above
2. Operating System: Windows 10 (64-bit) or above
Dataset Link:
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Link:- https://www.kaggle.com/datasets/deboleenamukherjee/financial-risk-data-large
Note:
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[1] If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
[2] Please note that this implementation plan does not include any further steps after it is put into implementation.
[3] Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
[4] If the above plan satisfies your requirement please confirm with us.
We perform an Existing Approach Reference-3:Title :- A hybrid metaheuristic optimised ensemble classifier with self organizing map clustering for credit scoring
