Tech-with-Vidhya / credit-risk-assessment-fintech-framework-using-deep-learning-and-transfer-learningLinks
This project represents the credit risk assessment dual framework of predicting credit scores and the forecasts of credit default risk of the consumers of the financial institutions like commercial banks and lending firms. The implementation is dealt that mimics the real-world FICO Scoring Model with the custom enhancements to include lender's i…
☆17Updated 3 years ago
Alternatives and similar repositories for credit-risk-assessment-fintech-framework-using-deep-learning-and-transfer-learning
Users that are interested in credit-risk-assessment-fintech-framework-using-deep-learning-and-transfer-learning are comparing it to the libraries listed below
Sorting:
- Geoffrey-Z / Multivariate-Time-Series-Forecasting-with-LSTMs-in-Keras-for-CORN-SWEET-Terminal-Market-Price☆16Updated 4 years ago
- Credit card Fraud Transactions using GANs and WGANs☆11Updated 6 years ago
- RNN - Stock Prediction Model using Attention Multilayer Recurrent Neural Networks with LSTM Cells☆40Updated 8 years ago
- Please run python train.py to predict time series.☆10Updated 6 years ago
- LSTM-XGBoost Time Series Forecasting☆146Updated last year
- Univariate_ARIMA_models, ARCH/GARCH Volatility Forecasting models, VAR model for macro fundamentals forecasts☆12Updated 4 years ago
- Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The…☆24Updated 7 years ago
- Comparing Long Term Short Memory (LSTM) & Gated Re-current Unit (GRU) during forecasting of oil price .Exploring multivariate relationsh…☆48Updated 3 years ago
- Exponential Smoothing, SARIMA, Facebook Prophet☆12Updated 4 years ago
- Comparing XGBoost, CatBoost and LightGBM on TimeSeries Regression (RMSE, R2, AIC) on two different TimeSeries datasets.☆23Updated 6 years ago
- Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebo…☆155Updated 2 years ago
- Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time ser…☆21Updated 8 years ago
- Wasserstein GAN with gradient penalty (WGAN-GP) applied to financial time series.☆17Updated 6 years ago
- This machine learning model (LSTM Time Series model) helps us to forecast demand of a supply chain business problem. This model uses Kera…☆29Updated 7 years ago
- stock prediction with GAN and WGAN☆103Updated 3 years ago
- Predict seasonal item sales using classical time-series forecasting methods like Seasonal ARIMA and Triple Exponential Smoothing and curr…☆31Updated 5 years ago
- A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series☆72Updated 6 years ago
- Algorithms proposed in the following paper: OLIVEIRA, Gustavo HFMO et al. Time series forecasting in the presence of concept drift: A pso…☆12Updated 4 years ago
- BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture,…☆71Updated 7 years ago
- Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is us…☆434Updated 5 years ago
- Financial time series forecast using dual attention RNN☆27Updated 6 years ago
- I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The A…☆60Updated 5 years ago
- ☆78Updated 5 years ago
- Stock Price Prediction using CNN-LSTM☆86Updated 5 years ago
- This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking a…☆53Updated 3 years ago
- Time-Series models for multivariate and multistep forecasting, regression, and classification☆62Updated 3 years ago
- This repository contains implementation of some techniques like SMOTE, ADASYN, SMOTE + Tomek Links, SMOTE + ENN to overcome class imbalan…☆47Updated 5 years ago
- ☆81Updated 3 years ago
- This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.☆85Updated 4 years ago
- PHBS 2018 Machine Learning Class Project☆14Updated 7 years ago