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Udai Sharma's Projects

building-machinelearning-models icon building-machinelearning-models

In this repository, I aim to implement various machine learning models entirely from scratch using object-oriented programming (OOP) principles. Instead of relying on pre-built libraries like scikit-learn or TensorFlow

falling-into-a-black-hole icon falling-into-a-black-hole

This project investigates the nature of black hole singularities and the fate of an infalling observer. We explore the existence of strong and weak singularities, challenging the prevailing notion of a singularity as an unavoidable annihilation point.

heart-disease-prediction icon heart-disease-prediction

This project showcases the implementation of a Logistic Regression model for heart disease prediction, highlighting proficiency in data handling, model training, and evaluation using Python.

human-activity-prediction icon human-activity-prediction

This project focuses on using sensor data to predict human activity and is based on the ExtraSensory dataset, created by Ph.D. students and staff at the Department of Electrical and Computer Engineering, University of California, San Diego.

hypothesis-testing-using-python icon hypothesis-testing-using-python

This project summarizes the findings of an A/B testing experiment comparing a Light Theme and a Dark Theme for a website. We investigated user behavior metrics to understand which theme might be more effective.

monte-carlo-model-for-criticality-conditions icon monte-carlo-model-for-criticality-conditions

This project aims to optimize the accuracy of critical size estimations by understanding and minimizing statistical fluctuations and transient effects. Once an optimal strategy is devised, the focus shifts to implementing a cylindrical geometry with a height equal to the radius and using the Monte Carlo code to estimate the critical length.

non-linear-harmonic-oscillator icon non-linear-harmonic-oscillator

This project investigated the behavior of a nonlinear harmonic oscillator solver and explained the observed loss of accuracy under certain conditions. It extended a linear harmonic oscillator solver to a nonlinear counterpart using the model 'Method of Manufactured Solutions'.

spam-mail-prediction icon spam-mail-prediction

This project leverages Logistic Regression to analyze email content and distinguish between legitimate emails (ham) and spam.

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