mencarellic

global
    maxconn 250
    daemon
    log stdout format raw local0

defaults
    log global
    mode http
    option httplog
    timeout connect 5s

mencarellic

version: "3.8"

services:

  haproxy:
    image: haproxy:2.3
    ports:
      - "80:80"
      - "8000:8000"
    volumes:

mencarellic

import pandas as pd
import numpy as np
import re
import nltk
from sklearn.feature_extraction.text import CountVectorizer
from collections import Counter

# Show all columns and do not truncate in a DF
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', -1)

mencarellic

import numpy as np
np.random.seed(1000)

from matplotlib import pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
sns.set_palette((sns.color_palette('colorblind', 8)))
dims = (11.7, 8.27)
%matplotlib inline

mencarellic

from imutils import paths
import face_recognition
import pickle
import cv2
import os
import imghdr

## Path to the training data stored with each person in their own folder
### Example: FaceData/Carlo_Mencarelli/image.png
imgpath = 'FaceData/'

mencarellic

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plot
import string

df = pd.read_csv("C:\git\IST718\Week2_Class\Coaches.csv")
df.fillna(0, inplace=True)
df['TotalSalary'] = df['TotalSalary'].str.replace('$','')
df['TotalSalary'] = df['TotalSalary'].str.replace(',', '')

mencarellic

library(neuralnet)
setwd("D:/Syracuse/SCM651/Homework")
data <- read.csv("Business+Analytics+-+Week+9+Universal+Bank.csv")

## Formulas
# Basic - all variables
basicFormula <- as.formula(PersonalLoan~Age+Income+Family+CCAvg+Education+Mortgage+SecuritiesAccount+CDAccount+Online+CreditCard+Experience)
# Only significant variables
basicFormulaOnlySig <- as.formula(PersonalLoan~Income+Family+CCAvg+Education+SecuritiesAccount+CDAccount+Online+CreditCard)
#basicFormulaLogitOnlySig <- as.formula(PersonalLoan~Income+Family+CCAvg+Education+SecuritiesAccount+CDAccount+Online+CreditCard)