ASHOK's starred repositories
Forex-Rate-Prediction-between-USD-INR-pair
This project consists of an implementation of various sequence models to predict the Forex rate between the USD/INR pair.
Forex-USD-INR
Forex forecast for USD to INR
Time-Series-Forecasting---INR-vs.-USD-Exchange-Rate
In this study, valuation of the Indian National Rupee (INR) has been analyzed against the US Dollar (USD).
LSTM-Forex-Prediction
A simple stacked LSTM model for predicting 3 timesteps in advance for EURUSD pairs.
forex-price-prediction
Predict and analyze historical XAU/USD Forex prices using deep learning; make decisions on short/long positions with target profit and stop loss values.
Fake-News-Detective
Fake News Detective uses NLP to identify and debunk fake news, helping people to stay informed and make informed decisions. It is a powerful tool in the fight against misinformation.
Predict-the-house-prices-in-India
In this project we solve the challenge posted on Kaggle to predict the price of house. In this project we make of models like linear regression, gradient boosting, random forest and decision tree.
predicting-house-prices-in-bengaluru
Analysis and prediction of house prices of Bengaluru - India.
House_Price_prediction
Analysis and Model evaluation of House price in India.
Credit_Risk_Analysis
We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
Synthetic-financial-data
This repository contains python code used to create synthetic data samples of minority class for a financial dataset. It also contains a sample of generated synthetic data.
Credit-card-fraud-detection-using-Federated-Learning-and-Split-Learning
Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch
Credit-Card-Fraud-Detection
Fraud Detection model based on anonymized credit card transactions
Housing-Prices-Advanced-Regression-Techniques
This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.
Credit-Risk-Analysis
Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods
credit-risk-analysis
The aim is to understand which are the key factors for a certain level of credit risk to occur. In addition, some ML models capable to predict the credit risk level for a company in an year - given past years data - have been built and compared.
Credit-Risk-using-RF-ANN
Credit Risk from loan data 2007-2014
CredtRiskAnalysis
The "Credit Risk Analysis" project aims to develop an Artificial Neural Network (ANN) model to assess credit risk for potential borrowers. The notebook utilizes TensorFlow to build the model, leveraging a dataset containing various financial attributes of applicants.
credit-card-loan-risk-analysis
Determine whether a new loan applicant will be able to repay their debt or not. Manipulated and visualized data, performed data pre-processing for a very small dataset of 50,000 applicants. Trained many supervised models like Random Forest, Boosting ensemble learning with LightGBM, XGBoost and CatBoost, and Stacked ensemble learning with Soft Voting and Stacked models achieving +0.64 ROC AUC. Compared that result against a Deep Learning neural network like a Multilayer perceptron. Deployed in AWS instances using Docker and also using API-based web-service application with Flask.
credit-risk-modelling
Credit Risk analysis by using Python and ML
VerticalFederatedLearning
Evaluating Collaborative Forecasting using Non-Horizontal Federated Learning
news-recommendation-engine
Developing a news recommendation engine by incorporating the fundamentals of Federated learning
federated-learning-backdoor-attack-defense-mechanism
Defending Against Federated Learning Backdoor Attacks: Defense Strategies and Performance Evaluation