Divyanshu Pabia's Projects
100 Days of Code: The Complete Python Pro Bootcamp
This repository contains the design and implementation of a fully operational processor created using VHDL (VHSIC Hardware Description Language). This processor is designed to execute a wide array of machine-level instructions effectively.
Bitcoin Core integration/staging tree
An app to predict weather conditions based on extensive data sets
The program generates optimal clusters using a grey-wolf optimizer (GWO) and perform clustering of IRIS dataset.
Solutions to problems I've solved on coding platforms
The program uses particle swarm optimization (PSO) algorithm to perform clustering based segmentation of colors in an image.
This project is waiting for your contribution. If you have never contributed code on GitHub before, this is the project to get you started.
This repository is dedicated to a specialized multi-label classifier using the Support Vector Machine (SVM) algorithm. The model is engineered to identify the appropriate type of actuator based on various input features, including Stress, Strain, and Efficiency, among others.
Sharpen your problem solving skills by learning core computer science concepts in a pain-free manner
Built a program to detect edges in an image using the properties of high and low-pass filters, 2D convolutions, Gaussian kernels, Fourier transforms, etc. The novel part about this project was that we did not use the built-in OpenCV library functions, we implemented the whole program ourselves, from scratch.
Python code for feature selection using gravitational search algorithm (GSA) and support vector machine (SVM) on the IRIS dataset. To run: python optimizer.py For non-commercial purposes.
πβ¨ Help beginners to contribute to open source projects
A Machine Learning powered app to decide whether a photo is food or not.
Starter repo for task 1 of the JPMC software engineering program
Starter repo for task 2 of JPMC's forage program
Starter repo for task 3 of JPMC's Forage program
The "Google Trends Analysis for Machine Learning" project is a data-driven exploration of the popularity and regional interest in machine learning as a subject of inquiry on Google Search. This project utilizes Python and the Google Trends API to gather and visualize data regarding search interest in machine learning topics over time.
This repository contains a Python code for detecting hate speech in text. The code is based on a decision tree classifier and has been trained on a dataset of tweets that have been labeled as hate speech or not. The code is written in Python and uses the scikit-learn library.
Implemented the merge sort algorithm in Assembly language, sorting sets of strings given in input files, lexicographically. This was simulated using ARMSim.
This repository contains an implementation of a neural network designed for multi-class classification tasks, specifically trained on the Fashion-MNIST dataset. Fashion-MNIST is a dataset curated by Zalando that consists of 28x28 grayscale images of clothing items, categorized into 10 distinct classes.
This repository features an implementation of a text classification model using the Naive Bayes algorithm. The classifier is trained to discern the sentiment of movie reviews from a large dataset, categorizing each review as either positive or negative.
Neural Networks: Zero to Hero
Segmenting objects in an image using differential evolution which are further classified into various shapes using Support Vector Machine.
Your image is almost there!
This program uses Superpixel to identify uniform regions in an image which are further analysed for the detection of road using neural network.