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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) |
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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) |
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<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; | |
} |
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<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; |
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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 |
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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(): |
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<% | |
if (s.forceSsl) { | |
%> | |
server { | |
listen 80; | |
server_name <%-s.publicDomain%>; | |
# Used by Lets Encrypt |