Heart Attack Analysis & Prediction using Artificial Neural Network

A forecasting system for multiple sectors that uses ARIMA, ETS, SVR, and other models displayed on a user friendly interface with different viewing options.

AIKA is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses…

Analyze the customer data, build a neural network to help the operations team identify the customers that are more likely to churn, and provide recommendations on how to retain such customers

This code uses computational graph and neural network to solve the five-layer traffic demand estimation in Sioux Falls network. It also includes comparison of models and 10 cross-validations.

[ICLR 2024] Dynamic Neural Response Tuning

Lightweight simple implementation of Artificial Neural Network for Arduino ESP32, ESP8266, RP2040, PlayStation Portable SDK, WebAssembly, and even Windows, macOS, and Linux-based OSes.

Assignment Project for Machine Learning with Python course of freeCodeCamp

This could include projects related to meditation and mental health, such as guided meditation apps or tools for tracking mood and stress levels. The goal of "OpenConscience" would be to promote technology that helps people live more mindfully and ethically in their personal and professional lives.

The code for the work presented in the research paper titled "***"

This repo contains my work & The code base for this Deep Learning Specialization offered by deeplearning.AI

Timing of emergence of CONUS summertime temperatures

Built and tested 6 supervised machine learning algorithm to develop a predictive classification model to classify 13000+ projects as success or failure.

A collection of resources and papers on Diffusion Models.

Application to predict heart disease using logistic regression

Modelling volume of water consumed by poor people with multilinear regression and multilayer perceptron

This repository contains a comprehensive Python script for building and training an Artificial Neural Network (ANN) model to predict customer churn using customer data. The code includes extensive data preprocessing steps, such as scaling, one-hot encoding, and visualization, to ensure the model's accuracy and effectiveness.

Using patient data as a csv file, I have built machine learning models to predict heart disease. Predictions involve:

In this project, we will explore the implementation of a Multi Layer Perceptron (MLP) using PyTorch. MLP is a type of feedforward neural network that consists of multiple layers of nodes (neurons) connected in a sequential manner.