rajasekharmekala

sherlocked

Investigating various properties of neural networks

attention_visualizer

A tool to visualize attention on text prompts for LLMs using huggingface

Diabetes-data-analysis

Abstract In this project, we plan to analyze the problem of predicting hospital readmission rates among diabetic patients using the "Diabetes 130-US hospitals" dataset. Tradi- tionally, this problem is dealt with by using statistical machine learning algorithms like Naive Bayes, K-Nearest Neighbors, and Logistic regression. These algorithms are known to not perform well on non-separable and high-dimensional datasets. To overcome these pitfalls, we will explore advanced techniques such as random forests, ensemble methods, and neural networks. Missing data, overfitting, and feature engineering are some of the challenges that we will encounter. The ideal outcome of the project would be to gain deeper insights into hospital readmission rates and investigate robust methods that can make improved predictions than the statistical methods. Our experiments show that Random forests performed better than other methods in the predictions.Attributes like gender, race, total number of medications, lab procedures, admission type, time in hospital of the patient had a significant influence in these predictions.

echoprompt

journey-prioritization-demo

Adobe AJO Journey sample demo app

pagerank

Page Rank for Evolving graphs using an incremental algorithm

pix2pix-experiments

Implementation of pix2pix and experiments from various settings on the research paper

predicting-short-story-endings

quote-extractor

simple-2048-implementation

rajasekharmekala/echoprompt

rajasekharmekala/attention_visualizer

A tool to visualize attention on text prompts for LLMs using huggingface

rajasekharmekala/Diabetes-data-analysis

Abstract In this project, we plan to analyze the problem of predicting hospital readmission rates among diabetic patients using the "Diabetes 130-US hospitals" dataset. Tradi- tionally, this problem is dealt with by using statistical machine learning algorithms like Naive Bayes, K-Nearest Neighbors, and Logistic regression. These algorithms are known to not perform well on non-separable and high-dimensional datasets. To overcome these pitfalls, we will explore advanced techniques such as random forests, ensemble methods, and neural networks. Missing data, overfitting, and feature engineering are some of the challenges that we will encounter. The ideal outcome of the project would be to gain deeper insights into hospital readmission rates and investigate robust methods that can make improved predictions than the statistical methods. Our experiments show that Random forests performed better than other methods in the predictions.Attributes like gender, race, total number of medications, lab procedures, admission type, time in hospital of the patient had a significant influence in these predictions.

rajasekharmekala/pix2pix-experiments

Implementation of pix2pix and experiments from various settings on the research paper

rajasekharmekala/predicting-short-story-endings

rajasekharmekala/journey-prioritization-demo

Adobe AJO Journey sample demo app

rajasekharmekala/pagerank

Page Rank for Evolving graphs using an incremental algorithm

rajasekharmekala/quote-extractor

rajasekharmekala/rajasekharmekala.github.io

Forked from alshedivat /al-folio

rajasekharmekala/simple-2048-implementation

rajasekharmekala/Search-Engine

Information Retrieval assignment submissions

rajasekharmekala/tweet_gen

rajasekharmekala/Vision-Quest

Hand Writing Recognition Project using various Algorithms