jaganadhg

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

jaganadhg

import pylab as plt
import numpy as np
from scipy.spatial.distance import cdist, pdist
from sklearn.cluster import KMeans
from sklearn.datasets import load_iris

iris = load_iris()

k = range(1,11)

jaganadhg

Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates http://arxiv.org/abs/1510.03797
Practical Deep Text Learning https://dato.com/learn/gallery/notebooks/deep_text_learning.html?_ga=1.114440388.217578858.1445957127
Code to reproduce experiments from the EMNLP 2015 paper about rumour classification.  https://github.com/mlukasik/rumour-classification
Deep Learning for Natural Language Processing  https://github.com/attardi/deepnl
Deep Learning, NLP, and Representations http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/
ML Perceptron in sklearn https://github.com/scikit-learn/scikit-learn/pull/5214
Bayesian Optimization using xgboost and sklearn API  https://github.com/mpearmain/BayesBoost
Pipeline http://blaze.pydata.org/blog/2015/10/19/dask-learn/
TwitterAnomaly in Python https://github.com/wdm0006/pyculiarity
Comparing Python Clustering Algorithms https://github.com/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb

jaganadhg

The Foundations of Data Science http://data8.org/text/
Controled Experiemtn on Word Embedding http://arxiv.org/pdf/1510.02675v1.pdf 
Twitter NEr https://github.com/leondz/entity-recognition/ 

jaganadhg

http://www.stefvanbuuren.nl/publications/MICE%20in%20R%20-%20Draft.pdf
https://aclweb.org/anthology/D15-1311
http://www.cis.upenn.edu/~xwe/publications/tacl2015-text-simplification-opinion.pdf
https://github.com/ayoungprogrammer/readAI
https://aclweb.org/anthology/D/D15/D15-1086.pdf
https://www.linkedin.com/pulse/naive-bayes-classifier-foundation-machine-learning-chase-perkins
http://www.emnlp2015.org/best-papers.html
http://news.mit.edu/2015/data-analytics-online-shopping-0915#.Vfh5mCy1iiM.twitter
http://nlp.stanford.edu/projects/glove/
http://www.slideshare.net/isabelleaugenstein/extracting-relations-between-nonstandard-entities-using-distant-supervision-and-imitation-learning

jaganadhg

https://medium.com/rants-on-machine-learning

jaganadhg

from __future__ import division
import pandas as pd


def missing_value_ratio(pd_series):
    """
    :param pd_series: a Pandas Series object
    :return ratio: float ; missing value ratio
    Find the missing value ratio.
    missing value ratio = no of missing value / total number of rows

jaganadhg

1) Independent component analysis: an introduction http://u.cs.biu.ac.il/~louzouy/courses/seminar/ica.pdf
2) Principal component analysis https://www.utdallas.edu/~herve/abdi-awPCA2010.pdf
3) The Seven Practice Areas of Text Analytics http://datamininglab.com/images/pdfs/PracticalTextMining_Excerpt.pdf
4) Statistical analysis made easy in Python with SciPy and pandas DataFrames http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/
5) Pearson Correlation http://www.statsoft.com/Textbook/Statistics-Glossary/P/button/p#Pearson%20Correlation
6) THE PEARSON CHI-SQUARED TEST WITH PYTHON AND R http://connor-johnson.com/2014/12/31/the-pearson-chi-squared-test-with-python-and-r/
7) 7 Techniques for Dimensionality Reduction http://www.dataminingreporting.com/blog/7-techniques-for-dimensionality-reduction 
https://www.linkedin.com/grp/post/35222-5998794653007171586
8) An Introduction to Distributed Machine Learning http://blog.dato.com/an-introduction-to-distributed-machine-learning-1
9) K-near

jaganadhg

https://github.com/saimacs/curtailment 

http://nbviewer.ipython.org/url/jakevdp.github.io/downloads/notebooks/SeattleCycling2.ipynb 


https://github.com/paulgb/sklearn-pandas

http://ramiro.org/notebook/mapping-pubs/?imm_mid=0d697c&cmp=em-data-na-na-newsltr_20150812

https://github.com/BuzzFeedNews

jaganadhg

import glob
import urllib2
import zipfile


def cbd_downloader(url,path):
    """
    Download the Citi Bike Station Data.
    :param url: string url of the data file
    :param path: string - path to save the file