Kunal Bandale's Projects
๐ฑ Android-Mini-Projects ๐ A collection of bite-sized Android projects for learning and experimentation.
JS Animation Project using GSAP Library
:eyeglasses: A curated list of awesome android projects by open-source contributors.
This repo contains all the project related to blockchain technology.
This repository contains my everyday C , C++ and DSA codes.
Web Developement
Repository for documenting my Data Structures and Algorithms journey in C++.
This repository is a collection of my Excel-based projects, showcasing various data analysis, visualization, and automation solutions implemented using Microsoft Excel.
# using this repo to use git lfs to host large files
A collaborative space for Git and Data Structures & Algorithms enthusiasts to practice coding, learn version control with Git, and contribute their DSA implementations. Let's code and learn together!
This repository contains a collection of Java codes that I write daily to improve my programming skills. Each code has been reviewed and guided by the Head of the Department (HOD) to ensure that it meets the highest standards of quality and adheres to industry best practices.
This repo contains all my homework , practical's and assignments about machine learning assigned by my college.
Notes made by me on different topics are uploaded in this repo
Welcome to my Daily Python Code Repository! Here, I'll be sharing my daily Python code snippets, projects, and experiments. ๐
This Python Project Repository contains various Python projects aimed at exploring different aspects of programming in Python.
Detect Email/SMS Spam with Machine Learning!
StyleMate is an intelligent fashion recommender system designed to help users discover their perfect outfits effortlessly. Leveraging deep learning techniques, StyleMate generates image embeddings for fashion items, allowing for accurate recommendations based on visual similarities.
This project aims to develop a watch price prediction system using machine learning regression techniques. The research will employ fundamental techniques such as Linear Regression, Decision Tree, Random Forest, and XGBoost to predict the prices of wristwatches and pocket watches.