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adaptive-forex-forecast

An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms

ann-implementations

This repository contains my implementations of various types of artificial neural networks.

burger-builder

A demo application that allows you to build a burger and order it

cbic-cgst-zone-allocation

champ

𓃡 Quadruped Robot based on MIT Cheetah I

classification-algorithms

My implementations of various Machine Learning classification algorithms

conversational-form

Turning web forms into conversations

etch-a-sketch

evolutionary-algorithms

feed-reader

flutter_assignment_1

flutter_complete_guide

insta-bot

A basic python bot for instagram account management

k-means-clustering

Implementation of K-Means clustering algorithm in python

maze-solving-robot

movie-rentals-app

This is an imaginary service for renting out movies

movie-trailer-website

my-portfolio

my-resume-js

naive-image-compression

This project is an implementation of a naive image compression algorithm based on singular value decomposition of an image

oops-lab-assignments

os-lab-assignments

OS-Lab-Assignments

particle-swarm-optimization

This repository contains my implementation of the particle swarm optimization algorithm

project-website-optimization-udacity

python-nse-option-chain-analyzer

The NSE has a website which displays the option chain in near real-time. This program retrieves this data from the NSE site and then generates useful analysis of the Option Chain for the specified Index or Stock. It also continuously refreshes the Option Chain and visually displays the trend in various indicators useful for Technical Analysis.

spam-sms-classifier

Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'. In this project we will be using the Naive Bayes algorithm to create a model that can classify dataset SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like. Usually they have words like 'free', 'win', 'winner', 'cash', 'prize' and the like in them as these texts are designed to catch your eye and in some sense tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us! Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.

todo

udacity-maps-project

website-optimization