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automatic-text-summarizer

Automatic Document Summarizer using Bipartite HITS, Natural Language Processing (NLP)

berkeleyx-cs100.1x-big-data-with-apache-spark

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

churn_prediction

SVM, RF and KNN to predict customer churn

churnyhat

Churn prediction example by yHat

cricket-match-prediction

cs221project

The CS221 Project

cs224u-project

Final project for CS 224U, Spring 2015

dat4

General Assembly's Data Science course in Washington, DC

data-science

Data Science Projects

data-science-capstone

The Swiftkey Capstone project for the Coursera Data Science Specialization

dataset-examples

Samples for users of the Yelp Academic Dataset

datasharing

The Leek group guide to data sharing

discussionsummarization

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.

gateinspring

hadoop

Mirror of Apache Hadoop

kaggle-otto-group-product-classification

Machine Learning final project. Messed around with various classification methods.

kaggle-otto-group-product-classification-challenge

Kaggle: Otto Group Product Classification Challenge

machine_learning_examples

A collection of machine learning examples and tutorials.

model-tech-stocks

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)..

otto-group-product-classification-kaggle

Classify products based on correct categories

predicting-the-outcome-of-cricket-match

This project aims to predict the outcome of a cricket match given the team configurations and the previous performances of the players.

prediction-of-stock-prices-using-time-series-models

Liner Regression, Holt Winters and Arima Models are used for prediction

project1

Test project

python-exercises

Series of basic exercises on python

python-machine-learning-book

The "Python Machine Learning" book code repository and info resource

sentiment-analysis

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.

sentiment-analysis-benchmark

This code provides the source code and datasets to perform a sentiment analysis benchmark between various NLP APIs.

sentiment_analysis_python

Working with sentiment analysis in Python.

spark

Mirror of Apache Spark

stockpredictor

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.