Aditya Damar Jati's Projects
Web scraping is a technique of taking data from a website automatically using a computer program. The data retrieved can be in the form of anything, including text. From the text, we can also do analysis to get insight by using sentiment analysis, bigram, trigram, topic modeling, and so on.
This is my track record following a DATA SCIENCE bootcamp from DPHI
Config files for my GitHub profile.
Data-Driven Price Prediction and Market Segmentation of Air BnB so both host and guest know about the price in the market.
End-to-end real time project from hicounselor
Web Portofolio & Blog dibuat menggunakan NextJS dan Tailwind CSS
Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch
Analytics Vidhya -Janata Hack-Hr Analytics #15th Solution
Awesome lists about Project Management interesting and useful topics.
📱 ✅ Some awesome projects in python! 📱 ✅
A python HEASOFT wrapper for processing Swift-BAT data.
Creative Commons Licenses for Github
This is just for study Cohort Analysis with python
This repository contains code I wrote for all the competitions that I participated in
I try learn a course that focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
The Python programming language
Credit risk analysis for credit card applicants
Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods
This is where I tried different Machine Learning methods to predict loan defaults
OroCRM - an open-source Customer Relationship Management application.
Final Project Rakamin Academy Data Science Bootcamp Batch 25
I tried to train my self using someone project.
Data Analysis Using Python: A Beginner’s Guide Featuring NYC Open Data
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent