A Study on Prediction Model of Peer-to-Peer (P2P) Social Lending Debtor Default using Deep Learning Technique

Peer-to-Peer (P2P) lending transactions take place by the lenders choosing a borrower and lending money. It is important to predict whether a borrower can repay because the lenders must bear the credit risk when the borrower defaults, but it is difficult to design feature extractors with very complex information about borrowers and loan products. In this paper, we present an architecture of deep convolutional neural network (CNN) for predicting the repayment in P2P social lending to extract features automatically and improve the performance. CNN is a deep learning model for classifying complex data, which extracts discriminative features automatically by convolution operation on lending data. We classify the borrower’s loan status by capturing the robust features and learning the patterns. Experimental results with 5-fold cross-validation show that our method automatically extracts complex features and is effective in repayment prediction on Lending Club data. In comparison with other machine learning methods, the standard CNN has achieved the highest performance with 75.86%. Exploiting various CNN models such as Inception, ResNet, and Inception-ResNet results in the state-of-the-art performance of 77.78%. We also demonstrate that the features extracted by our model are better performed by projecting the samples into the feature space.

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Telecom Churn Prediction Using Seven Machine Learning Experiments integrating Features engineering and Normalization

10.21203/rs.3.rs-239201/v1 ◽

2021 ◽

Author(s):

Hemlata Jain ◽

Ajay Khunteta ◽

Sumit Private Shrivastav

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Logistic Regression ◽

Deep Learning ◽

Random Forest ◽

Prediction Model ◽

Confusion Matrix ◽

Hybrid Models ◽

Churn Prediction ◽

Learning Technique

Abstract Machine Learning and Deep learning classification has become an important topic in the area of Telecom Churn Prediction. Researchers have come out with very efficient experiments for Churn Prediction and have given a new direction to the telecommunication Industry to save their customers. Companies are eagerly developing the models for predicting churn and putting their efforts to save the potential churners. Therefore, for a better churn prediction model, finding the factors of churn is very important. This study is aiming to find the factors of user’s churn by evaluating their past service usage details. For this purpose, study is taking the advantage of feature importance, feature normalisation, feature correlation and feature extraction. After feature selection and extraction this study performing seven different experiments on the dataset to bring out the best results and compared the techniques. First Experiment includes a hybrid model of Decision tree and Logistic Regression, second experiment include PCA with Logistic Regression and Logit Boost, third experiment using a Deep Learning Technique that is CNN-VAE (Convolutional Neural Network with Variational Autoencoder), Fourth, fifth, sixth and seventh experiments was done on Logistic Regression, Logit Boost, XGBoost and Random Forest respectively. First four experiments are hybrid models and rest are using standalone techniques. The Orange dataset was used in this technique which has 3333 subscriber’s entries and 21 features. On the other hand, these experiments are compared with already existing models that have been developed in literature studies. The performance was evaluated using Accuracy, Precision, Recall rate, F-measure, Confusion Matrix, Marco Average and Weighted Average. This study proved to get better results as compared to old models. Random Forest outperformed in this study by achieving 95% Accuracy and all other experiments also produced very good results. The study states the importance of data mining techniques for a churn prediction model and proposes a very good comparison model where all machine Learning Standalone techniques, Deep Learning Technique and hybrid models with Feature Extraction tasks are being used and compared on the same dataset to evaluate the techniques performance better.

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Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Computer Systems Science and Engineering ◽

10.32604/csse.2022.021606 ◽

2022 ◽

Vol 42 (1) ◽

pp. 257-269

Author(s):

R. Kanthavel ◽

R. Dhaya

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Disease Diagnosis ◽

Learning Technique

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A deep learning technique-based data-driven model for accurate and rapid flood prediction

10.5194/hess-2021-596 ◽

2022 ◽

Author(s):

Qianqian Zhou ◽

Shuai Teng ◽

Xiaoting Liao ◽

Zuxiang Situ ◽

Junman Feng ◽

...

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Hydrodynamic Model ◽

Bayesian Optimization ◽

Data Driven ◽

Real Time Control ◽

Flood Prediction ◽

Urban Flood ◽

Learning Technique ◽

Flood Maps

Abstract. An accurate and rapid urban flood prediction model is essential to support decision-making on flood management, especially under increasing extreme precipitation conditions driven by climate change and urbanization. This study developed a deep learning technique-based data-driven flood prediction model based on an integration of LSTM network and Bayesian optimization. A case study in north China was applied to test the model performance and the results clearly showed that the model can accurately predict flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The model predicted flood maps 19,585 times faster than the physical-based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case, the difference between the ground truth and model prediction was only 0.76 % and the spatial distributions of inundated paths and areas were almost identical. The proposed model showed a robust generalizability and high computational efficiency, and can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real time control, optimization and emergency design and plan.

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