This is Mudit Vyas worked as ML Developer Intern , Team Leader in Technocolabs Software.This is Internship Project For Technocolabs Software.
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Updated
Feb 21, 2022 - Jupyter Notebook
This is Mudit Vyas worked as ML Developer Intern , Team Leader in Technocolabs Software.This is Internship Project For Technocolabs Software.
PROJECT OF STATISTICS FOR DATA SCIENCE
Machine Learning Projects for Finance-related use cases
Project of a coursework - Multivariate Analysis (M.Stat Semester 2) under the supervision of Prof. Swagata Nandi. (Project Group : Adrija Saha, Shrayan Roy, Sampurna Mondal)
Application of Machine Learning models to predict Company Bankruptcy
Accurately predicting companies' future failure with Python.
Corporate Bankruptcy Prediction
This project aims to determine the likelihood of a company facing bankruptcy, a crucial aspect of financial analysis and investment decision-making.
Classification model for Taiwanese companies bankruptcy, written in Jupyter Notebook using R.
Bankruptcy prediction - LDA, logit, NN
Company Bankruptcy Prediction with Naïve Bayes Algorithm
Bankruptcy Prediction project, exploring sampling and imputation techniques, aimed at improving recall, for MTL782 (Data Mining) course offered in Spring 2021
This repo contains all the mini projects that I had to do while pursuing my Masters in Data Science. They helped me better actuate the knowledge that I gathered while pursuing my Masters Degree
A smart system with a goal of predicting whether a company will go bankrupt in a certain number of years by leveraging the financial historical data of the Polish companies' bankruptcy dataset.
Our project aims to utilize machine learning techniques for bankruptcy prediction, leveraging data analysis and predictive models to forecast the likelihood of a company experiencing financial insolvency.
R: Bankruptcy prediction - Polish and Slovak companies
Prediction bankruptcy of Taiwanese bank in the years 1999 to 2009.
Design and implementation of a dashboard to display AI-based bankruptcy forecasting with the help of multiple and differentiated key figures about the company situation
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