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alocc-cvpr2018

Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC)

boston-housing-deep-learning

disaster_response_message_classifier

efficient_maintainance

The project objective is to enhance the maintenance operations and planning of time-based preventive maintenance by applying data science techniques and machine learning algorithms for predicting more accurate maintenance requirements.

gperftools

Main gperftools repository

hacker-earth

added code files

hand_talk_translator

This project is to helps the deaf person to communicate with a person using tactile signs. This project mainly has two primary tasks machine learning part which helps to translate the image gestures into corresponding word or sentence. Using tensorflow lite we deploy this machine learning model on to to the Android mobile device.

imdb-dataset

library-webapplication

This is an application used to maintain book list . This was developed using MySQL and backend database which has tables, Stored procedures and triggers. For middle I have used python to access the database. The UI is developed so simple, It developed using HTML and Bootstrap for styling purpose.

phishing_attack_detection_with_naturallanguageprocessing

Phishing websites are fraudulent sites that impersonate a trusted party to gain access to sensitive information of an individual person or organization. Traditionally, phishing website detection is done through the usage of blacklist databases. However, due to the current, rapid development of global networking and communication technologies, there are numerous websites and it has become difficult to classify based on traditional methods since new websites are created every second. In this paper, we are proposing a real-time, anti-phishing system. In the first step, we extract the lexical and host-based properties of a website. In the second step, we combine URL (Uniform Resource Locator) features, NLP and host-based properties to train the machine learning and deep learning models. Our detection model is able to detect phishing URLs with a detection rate of 94.89%.

phishing_dataset

recommendation_system

simple-salesforce

A very simple Salesforce.com REST API client for Python

suryasrikar.github.io

titanic-problem

This is the titanic kaggle dataset provided.