David Axelrod daxaxelrod

## .conf
<%
if (s.forceSsl) {
%>
    server {

        listen       80;

        server_name  <%-s.publicDomain%>;

        # Used by Lets Encrypt

## wav_headers.py
import os
import struct
import logging

def PrintWavHeader(strWAVFile):
    """ Extracts data in the first 44 bytes in a WAV file and writes it
            out in a human-readable format
    """
    def DumpHeaderOutput(structHeaderFields):
        for key in structHeaderFields.keys():

## nba_nn_keras.py
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# from sklearn.pipeline import Pipeline

## shawn_index2.html
<html>
<head>
<title>Basic Homepage</title>
<link href="style.css" rel="stylesheet" type="text/css">
<style type="text/css">


body {
text-align: center;
margin: auto;

## shawn_index.html
<html>
<head>
<title>Basic Homepage</title>
<link href="style.css" rel="stylesheet" type="text/css">
<style type="text/css">
body {
text-align: center;
margin: auto;
}

## latest_code_base.R
library(ggplot2)
library(lattice)
library(caret)
library(dplyr)

train <- read.csv(file="housing_prices/train.csv", header = TRUE, sep = ",")
test <- read.csv(file="housing_prices/test.csv", header = TRUE, sep = ",")

dim(train)
summary(train)

## gist:433ea07ebbf6dbcc8a6562189b3a2ab5
library(ggplot2)
library(lattice)
library(caret)
library(dplyr)

train <- read.csv(file="housing_prices/train.csv", header = TRUE, sep = ",")
test <- read.csv(file="housing_prices/test.csv", header = TRUE, sep = ",")

dim(train)
summary(train)
	<%
	if (s.forceSsl) {
	%>
	server {

	listen 80;

	server_name <%-s.publicDomain%>;

	# Used by Lets Encrypt
	import os
	import struct
	import logging

	def PrintWavHeader(strWAVFile):
	""" Extracts data in the first 44 bytes in a WAV file and writes it
	out in a human-readable format
	"""
	def DumpHeaderOutput(structHeaderFields):
	for key in structHeaderFields.keys():
	import pandas as pd
	import numpy as np
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.wrappers.scikit_learn import KerasRegressor
	from sklearn.model_selection import cross_val_score
	from sklearn.model_selection import KFold
	from sklearn.preprocessing import StandardScaler
	from sklearn.model_selection import train_test_split
	# from sklearn.pipeline import Pipeline
	<html>
	<head>
	<title>Basic Homepage</title>
	<link href="style.css" rel="stylesheet" type="text/css">
	<style type="text/css">


	body {
	text-align: center;
	margin: auto;
	library(ggplot2)
	library(lattice)
	library(caret)
	library(dplyr)

	train <- read.csv(file="housing_prices/train.csv", header = TRUE, sep = ",")
	test <- read.csv(file="housing_prices/test.csv", header = TRUE, sep = ",")

	dim(train)
	summary(train)