Jay Mahabal jmahabal

## .block
license: mit

## twitter.js
/* Setting things up. */
var path = require('path'),
    express = require('express'),
    app = express(),
    Twit = require('twit'),
    config = {
    /* Be sure to update the .env file with your API keys. */
      twitter: {
        consumer_key: process.env.CONSUMER_KEY,
        consumer_secret: process.env.CONSUMER_SECRET,

## twitter.py
from sklearn.externals import joblib
import tweepy
import json

def predict(tweet_text):
    # Load our model and vectorizer
    clf = joblib.load('model.pkl')
    vectorizer = joblib.load('vectorizer.pkl')

    # Make the retweet count prediction after vectorizing the tweet

## Data Cleaner.ipynb

      
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                jmahabal
                / Data Cleaner.ipynb
            
            
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              April 27, 2017 07:07
            
              
                NBA Margin of Victory
              
          
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## README.md

      
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                jmahabal
                / README.md
            
            
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              February 10, 2017 23:00
            
              
                Visual explanation of the importance of examining your data before trying to predict / run an algorithm.
              
          
    Visual explanation of the importance of examining your data before trying to predict / run an algorithm.
@JayMahabal / @H2Oai
Forked from 1wheel's block: you-draw-it

  
## index.html
<!Doctype HTML>

<head>

<!-- <script src="d3.js"></script> -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.4.1/d3.js"></script>

<style>

.triangle {

## grancairo.jpg

      
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                jmahabal
                / grancairo.jpg
            
            
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              January 18, 2017 07:56
            
              
                Frank Stella - Gran Cairo in d3.js
              
          
## .block
license: mit
height: 1000
scrolling: no
border:

## .block
license: mit
height: 600
	/* Setting things up. */
	var path = require('path'),
	express = require('express'),
	app = express(),
	Twit = require('twit'),
	config = {
	/* Be sure to update the .env file with your API keys. */
	twitter: {
	consumer_key: process.env.CONSUMER_KEY,
	consumer_secret: process.env.CONSUMER_SECRET,
	from sklearn.externals import joblib
	import tweepy
	import json

	def predict(tweet_text):
	# Load our model and vectorizer
	clf = joblib.load('model.pkl')
	vectorizer = joblib.load('vectorizer.pkl')

	# Make the retweet count prediction after vectorizing the tweet
	<!Doctype HTML>

	<head>

	<!-- <script src="d3.js"></script> -->
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.4.1/d3.js"></script>

	<style>

	.triangle {