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advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

applied-data-science-capstone

IBM Data Science Professional Certificate

battle-of-neighbourhoods

blockchain-development-for-finance-projects

Blockchain Development for Finance Projects, published by Packt

building-machine-learning-projects-with-tensorflow

Building Machine Learning Projects with TensorFlow by Packt

capstone-project

capstone-project---the-battle-of-neighborhoods

Capstone Project - The Battle of Neighborhoods

capstoneproject

This repository is used for the Coursera Capstone project

cecl-modelling-implementation

Forecasted the Expected Credit Loss, over the lifetime of the mortgage. Built Loan-level PD Model using Markov Chain Transition Matrix and logistic regression with six transition states and validated them using backtesting.

clustering-location-selection-for-a-new-restaurant

coursera-capstone

Final project for IBM Data SCience

coursera_capstone-1

Repository for work on IBM Data Science Professional Certificate Capstone Project

coursera_capstone-2

coursera_capstone-3

Coursework from Coursera: Applied Data Science Capstone

coursera_capstone-4

This notebook will be used for the Capstone Course Projects

coursera_capstone-5

This repository is part of the Coursera Capstone Project of IBM.

coursera_capstone-6

This repository contains my Coursera Capstone files.

coursera_capstone-7

This repository contains the data science Capstone project for the IBM Data Science Professional Certificate.

coursera_capstone-8

Capstone Project Notebook - Week 1

coursera_capstone-9

courses-

Quiz & Assignment of Coursera

credit-risk

Credit Risk - IRB Model Validation - BASEL Requirement

credit-risk-anaytics-the-r-companion

The rep contains the R files from Credit Risk Analytics: the R Companion, from H Scheule, D Rosch an B Baesens. Unfortunately, when I bought the book, I could not find the R files accompanying the book. So I decided to create the R files from the snippets of codes in the book. Hope you find the files useful. I also think this is an excellent book for those with an experience in both credt risk models and programming in R. If instead you want to learn R or credit risk, this is not the best book for you.

credit-risk-model

Credit Risk Model on Machine learning and prediction

credit-risk-model-1

Read Me File: 1. Type into the command line: cd Desktop 2. Drag the DecisionTree.java file along with all the data file on the Desktop ( Or skip the step 1 and directly drag DecisionTree. java file along with all the data file to the default directory path) 3.Type into the command line: javac DecisionTree.java (After this step , should generate three class file) 4.Type into the command line: java DecisionTree 1 training_set.csv validation_set.csv test_set.csv 1 (Or other argument or data file name that match the input argument format) Random Attribute Selection part: This part in the code starts from line 567. Unlike chooseBestAttribute method which choosing the largest IG of each attribute, chooseRandomtAttribute choose a random attribute column and return to the generate decision tree(line 431) method for next iteration, and we will use random generator to choose the random index and guarantee randomness.To test the decision tree based on random attribute selection, simply replace line 476 "chooseBestAttribute" with "chooseRandomAttribute".

credit-risk-modeling

credit-risk-modeling

credit-risk-modeling-using-machine-learning

A study and comparison of Risk Modeling algorithms (Capstone Project)

credit-risk.machine-learning-application.

This work aim to, on one side, the backend code develop on Python Notebook (code_credit_scoring_np.ipynb), for reason of readability, to find the best Hyperparameter in order to achieve the best forecast for the credit scoring with algorithm we analize during the thesis

creditcardanalysis

Credit card risk analysis logistic model The project includes Data exploration Missing. value treatment Model. building Model validation Business Interpretation

creditriskpaper

Codes for replication and implementation of techniques in our credit risk article