John Ifegwu johnifegwu

## email_extraction_&_validation.py
import re
#Character Classess

#email extraction / validation


#Email Address
pattern = r"([\w\.-]+)@([\w\.-]+)(\.[\w\.]+)"

while True:

## Passport Number_Extraction_and_Validation.py
import re
#Character Classess

#Passport number
pattern = r"[A-Za-z][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]"

while True:
    num = "Last Name: Doe \n"
    num += "Other names: John \n"
    num += "DOB:22-Jan-1990 \n"

## machine_learning.py
from numpy import exp, array, random, dot

class neural_network:
    def __init__(self):
        random.seed(1)
        # We model a single neuron, with 3 inputs and 1 output and assign random weight.
        self.weights = 2 * random.random((3, 1)) - 1

    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))

## prima-indians-diabetes.csv

          
            6
            148
            72
            35
            0
            33.6
            0.627
            50
            1

            
              1
              85
              66
              29
              0
              26.6
              0.351
              31
              0

            
              8
              183
              64
              0
              0
              23.3
              0.672
              32
              1

            
              1
              89
              66
              23
              94
              28.1
              0.167
              21
              0

            
              0
              137
              40
              35
              168
              43.1
              2.288
              33
              1

            
              5
              116
              74
              0
              0
              25.6
              0.201
              30
              0

            
              3
              78
              50
              32
              88
              31.0
              0.248
              26
              1

            
              10
              115
              0
              0
              0
              35.3
              0.134
              29
              0

            
              2
              197
              70
              45
              543
              30.5
              0.158
              53
              1

            
              8
              125
              96
              0
              0
              0.0
              0.232
              54
              1

## Sugar_Diabetes_Predictions.py
# my first neural network with keras
# requires that TensorFlow or Theano be installed
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
import numpy
import os

# fix random seed for reproducibility

## Draw_signature.html
<!-- Created by John Ifegwu-->

<!DOCTYPE HTML>
<html>
<header>
    <style>
        /* Created by John Ifegwu */

#canvas
{

## Quench_COVID19.py
class GameObject:

    class_name = ""
    desc = ""
    objects = {}

    def __init__(self, name):
        self.name = name
        GameObject.objects[self.class_name] = self


## Password_Validator.cs
using System;
using System.IO;
using System.Linq;
using System.Collections.Generic;

//Author: John Ifegwu
namespace MICKLE.Security
{

    public class PasswordValidator

## Calculator.php
<!DOCTYPE html>
<html>
   <head>
   <style>
       .Head
       {
         font-size:67px;
         width:100%;
         background:Gray;
         color:Silver

## Calculator2.html
<html>
   <head>
   <style>
       .Head
       {
         font-size:67px;
         width:100%;
         background:Gray;
         color:Silver
       }
	import re
	#Character Classess

	#email extraction / validation


	#Email Address
	pattern = r"([\w\.-]+)@([\w\.-]+)(\.[\w\.]+)"

	while True:
	import re
	#Character Classess

	#Passport number
	pattern = r"[A-Za-z][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]"

	while True:
	num = "Last Name: Doe \n"
	num += "Other names: John \n"
	num += "DOB:22-Jan-1990 \n"
	from numpy import exp, array, random, dot

	class neural_network:
	def __init__(self):
	random.seed(1)
	# We model a single neuron, with 3 inputs and 1 output and assign random weight.
	self.weights = 2 * random.random((3, 1)) - 1

	def __sigmoid(self, x):
	return 1 / (1 + exp(-x))
6	148	72	35	0	33.6	0.627	50	1
1	85	66	29	0	26.6	0.351	31	0
8	183	64	0	0	23.3	0.672	32	1
1	89	66	23	94	28.1	0.167	21	0
0	137	40	35	168	43.1	2.288	33	1
5	116	74	0	0	25.6	0.201	30	0
3	78	50	32	88	31.0	0.248	26	1
10	115	0	0	0	35.3	0.134	29	0
2	197	70	45	543	30.5	0.158	53	1
8	125	96	0	0	0.0	0.232	54	1
	# my first neural network with keras
	# requires that TensorFlow or Theano be installed
	from numpy import loadtxt
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.models import model_from_json
	import numpy
	import os

	# fix random seed for reproducibility
	<!-- Created by John Ifegwu-->

	<!DOCTYPE HTML>
	<html>
	<header>
	<style>
	/* Created by John Ifegwu */

	#canvas
	{
	class GameObject:

	class_name = ""
	desc = ""
	objects = {}

	def __init__(self, name):
	self.name = name
	GameObject.objects[self.class_name] = self
	using System;
	using System.IO;
	using System.Linq;
	using System.Collections.Generic;

	//Author: John Ifegwu
	namespace MICKLE.Security
	{

	public class PasswordValidator
	<!DOCTYPE html>
	<html>
	<head>
	<style>
	.Head
	{
	font-size:67px;
	width:100%;
	background:Gray;
	color:Silver