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A study and comparison of Risk Modeling algorithms (Capstone Project)
Credit Risk analysis by using Python and ML
This repository contains an approach (developed in Python) to credit risk analysis. The dataset was provided by a famous startup bank.
A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors
Using API, Collected Price data of Cryptocurrencies. Softwares used Python, libraries used Pandas, Numpy, Matplotlib, Glob.
🥚 Stanford CS221: Artificial Intelligence: Principles and Techniques
Code for Stanford CS228: Probabilistic Graphical Models
Assignments as part of CS 7646 at GeorgiaTech under Dr. Tucker Balch in Fall 2017
Independent study with databases using SQL
DataCamp data-science courses
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
In this repo, all my deep learning projects are there
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
Lifetime Expected Credit Loss calculation under IFRS 9
MSCA 32001 Financial Analytics - Finance projects in R
My solutions for the “C++ Programming for Financial Engineering” Online Certificate. It is a joint project by the Baruch MFE program, Dr. Daniel Duffy and QuantNet.
AI cases in Finance
Using Machine Learning for live currency trading
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Objective of this competition is to use historical loan application data to predict whether or not an applicant will be able to repay a loan.
In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. Maximum Accuracy achieved around 98.59%.
• Calculated the Capital Ratios,Risk Weighted Assets, Capital requirement over projected time horizon for both CCAR and CECL. • Created PD model using Time Series,Logistic regression,Random Forests,Neural Networks,Markov transition Matrix. • Software used various SAS 9.4, Python.
Solutions to the exercises in Grinstead and Snell's Introduction to Probability
R code for Kaggle's Loan Default Prediction - Imperial College London challenge
This is the Curriculum for "Learn Computer Science in 5 Months" By Siraj Raval on Youtube
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.