Anmol Panchal's Projects
Welcome to the ultimate SQL interview preparation repository for top tech companies like Google, Salesforce, Walmart, Metaverse, and Microsoft. This comprehensive study plan focuses on solving 50 essential SQL questions on LeetCode, providing you with the knowledge and practice needed to confidently tackle your SQL interview within just one month.
Machine Learning for Airfare Prediction
using selenium to scrape expedia and pandas to analyze data for lowest cost airfare and identifying price trends
Web development
Invitae Coding Challenge
💸💸 Curated list of investment & finance related resources
Basel Problem and harmonic Distribution in R
Sequence Alignment and DNA Sequence random shuffling
Box mueller Method , Acceptance Rejection Algorithm, Binomial Distribution implementation in python.
🤓 Build your own (insert technology here)
Simple C programs
This repository contains coding interviews that I have encountered in company interviews
Workday's Content Cloud validator tool
Cracking the Data Science Interview
Credit Card Fraud Detection using ANN in R
Hands-on, practical knowledge of how to use neural networks and deep learning with Keras 2.0
A Python script to convert H2 into MySQL.
My HackerRank solutions
This are the solutions to ALL "implementation" problems in the "algorithms" category at "https://www.hackerrank.com/"
Hackerrank Problem solving solutions in Python
The solutions of all SQL hackerrank challenges using MySQL environment
Everything you need to prepare for your technical interview
IoT- DHT11 readings using ESP32 and Arduino
IoT_App to monitor Environmental factors like Temperature, Humidity using dht11 sensor, GPS using ublox neo 6m gps sensor and pollutant measure using pmsa sensor.
IoT_ESP32 connection to University Eduroam WiFi
All beginners level java programs
Create, Send, Transform JSON Data
Java implementation for Minimum Hashing and LSH for finding near duplicates and false positives from documents as measured by Jaccard similarity. Locality-sensitive hashing (LSH) reduces the dimensionality of high-dimensional data. LSH hashes input items so that similar items map to the same “buckets” with high probability (the number of buckets being much smaller than the universe of possible input items). LSH differs from conventional and cryptographic hash functions because it aims to maximize the probability of a “collision” for similar items. Locality-sensitive hashing has much in common with data clustering and nearest neighbor search. Hashing-based approximate nearest neighbor search algorithms generally use one of two main categories of hashing methods: either data-independent methods, such as locality-sensitive hashing (LSH); or data-dependent methods, such as Locality-preserving hashing.