Abhishek Manoj Sharma's repositories
big-data-electricity-consumption-analysis-apache-spark
Developed for analysing and visualizing trends related to electricity and energy consumption
image-classification-artificial-intelligence
For classifying images into portraits or landscapes using a training dataset
NLP-Gutenberg-Character-Verb-Location-Relations-Visualization
Artificial Intelligence program to identify character-verb-character and character-location relations of an entire book from Gutenberg and visualizes them using NLTK (Natural Language Toolkit) and Matplotlib libraries respectively
tsp-algorithms-python-ai
Implementation of Greedy, Genetic, and A* algorithms in Python for finding the optimal path for a Travelling Salesman Problem
ai-knn-kmeans-crawler-python
This repository contains code for a program that scrapes through the pages of Indeed, IEEE Job Search, and ACM Job Search and displays the most relevant jobs based on the keywords provided. Also clusters them using k-means algorithm (LLoyd's algorithm).
Google-Chrome-Plugin-for-Sentiment-Analysis
Google Chrome plugin to perform sentiment analysis of amazon product reviews
android_device_samsung_maguro
Samsung Galaxy Nexus (GSM)
blog
Abhishek's Blog
leetcode-solutions-python
Solutions for LeetCode challenges in Python
Hangman-Android
A hangman game for Android where the player tries to try and guess the word by guessing one character at a time. The game contains different categories like World, Country Capitals, Indian State Capitals, Country Names, Cricket, Bollywood, and Football. The game also offers three different difficulty levels to player in an attempt to make the game more competitive, and has other basic options to enable or disable sound, pop-ups, etc.
knn-python-artificial-intelligence
This repository contains code for implementing kNN algorithm on KEEL (http://sci2s.ugr.es/keel/category.php?cat=clas) datasets using Python.
locate-my-store
Developed in 2015, the project aimed to predict the products a user may want to buy based on the geographical location, demographics, and day of the week. Data mining techniques were implemented to select physical stores based on the keywords provided. All data was stored on Microsoft Azure, and computations were done on the cloud for faster and efficient processing. The application was built for Android, iOS, and WP, and a desktop tool was developed for the shopkeepers to track and update their inventory.