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Automatic Document Summarizer using Bipartite HITS, Natural Language Processing (NLP)
This repository contains code files specifically IPython notebooks for the assignments in the course "Introduction to Big Data with Apache Spark" by UC Berkeley and Databricks on edX
SVM, RF and KNN to predict customer churn
Churn prediction example by yHat
The CS221 Project
Final project for CS 224U, Spring 2015
General Assembly's Data Science course in Washington, DC
Data Science Projects
The Swiftkey Capstone project for the Coursera Data Science Specialization
Samples for users of the Yelp Academic Dataset
The Leek group guide to data sharing
Discussion Summarization is the process of condensing a text document which is a collection of discussion threads, using CBS (Cluster Based Summarization) approach in order to create a relevant summary which enlists most of the important points of the original thematic discussion, thereby providing the users, both concise and comprehensive piece of information. This outlines all the opinions which are described from multiple perspectives in a single document. This summary is completely unbiased as they present information extracted from multiple sources based on a designed algorithm, without any editorial touch or subjective human intervention. Extractive methods used here, follow the technique of selecting a subset of existing words, phrases, or sentences in the original text to form the summary. An iterative ranking algorithm is followed for clustering. The NLP (Natural Language Processing) is used to process human language data. Precisely, it is applied while working with corpora, categorizing text, analyzing linguistic structure. Thus, the quick summary is aimed at being salient, relevant and non-redundant. The proposed model is validated by testing its ability to generate optimal summary of discussions in Yahoo Answers. Results show that the proposed model is able to generate much relevant summary when compared to present summarization techniques.
Mirror of Apache Hadoop
Machine Learning final project. Messed around with various classification methods.
Kaggle: Otto Group Product Classification Challenge
A collection of machine learning examples and tutorials.
An example model monitored by Ship Data Science! This one predicts the daily closing price of Google stock based on previous prices of Google, NASDAQ, and a commodities index (QQQ)..
Classify products based on correct categories
This project aims to predict the outcome of a cricket match given the team configurations and the previous performances of the players.
Liner Regression, Holt Winters and Arima Models are used for prediction
Test project
Series of basic exercises on python
The "Python Machine Learning" book code repository and info resource
This Project involves a process of analyzing sentiments about any particular movie using user reviews available on social networking sites like Facebook and Twitter into categories namely, Positive and Negative. The idea behind this was to help user make better judgement about the product by reading only positive reviews or negative reviews related to the product. Sentiment analysis involved extraction and measurement of the sentiment or “attitude” of a review using natural language processing steps such as stemming, stop-words removal and formation of similarity matrix using Stanford NLP libraries.
This code provides the source code and datasets to perform a sentiment analysis benchmark between various NLP APIs.
Working with sentiment analysis in Python.
Mirror of Apache Spark
Stock Prediction Application using SVM for prediction of Stock prices based on history. Also shows trends and graphs. Some Special features like StopLoss Recommendation, Trending stock are main features pf the Application.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.