Get a list of empty s3 buckets | aws | sh

Note: Before Running this script please configure your AWS CLI.

#!/bin/bash
for bucketlist in  $(aws s3api list-buckets | jq --raw-output '.Buckets[].Name');
do
  echo "* $bucketlist"
  listobjects=$(\
      aws s3api list-objects --bucket $bucketlist \
      --query 'Contents[*].Key')
  #echo "==$listobjects=="
    if [[ "$listobjects" == "null" ]]; then
          echo "$bucketlist is empty"
  echo $bucketlist >> emptyBucketList.txt
    fi
done

Regular Expression for Ration Card | RegEx | lex

Regular Expression: ^([a-zA-Z0-9]){8,16}\\s*$

Description: Easy expression that checks for valid Ration Card Number.

Regular Expression for Aadhar Card | RegEx | Lex

Regular Expression: ^[2-9]{1}[0-9]{3}[0-9]{4}[0-9]{4}$

Description: Easy expression that checks for valid Aadhar Card Number.

Regular Expression for Pan Card | RegEx | Lex

Regular Expression: ^([A-Z]){5}([0-9]){4}([A-Z]){1}$

Description: Easy expression that checks for valid Pan Card Number.

Regular Expression for Voter Id | RegEx | Lex

Regular Expression: ^([a-zA-Z]){3}([0-9]){7}$

Description: Easy expression that checks for valid Voter Id Number.

Regular Expression for Driving License | RegEx | Lex

Regular Expression: ^([a-zA-Z]){2}([0-9]){2}([0-9]){4}([0-9]){7}$

Description: Easy expression that checks for valid Driving License Number.

Regular Expression for Passport | RegEx | Lex

Regular Expression: ^([A-Z]){1}([0-9]){7}$

Description: Easy expression that checks for valid Passport Number.

Regular Expression for Email Id | RegEx | Lex

Regular Expression: (?:[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*)@(?:(?:[a-zA-Z0-9](?:[a-zA-Z0-9-]*[a-zA-Z0-9])?\\.)+[a-zA-Z0-9](?:[a-zA-Z0-9-]*[a-zA-Z0-9])?)

Description: Easy expression that checks for the valid email addresses.

Understanding Simple Linear Regression, Multiple Linear Regression and Polynomial Regression - ML

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd                                                                                      

dataset = pd.read_csv('D:\SEM 7\ML\data.csv')

X = dataset.iloc[:, :-1].values

Y = dataset.iloc[:, 1].values

fromsklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 1/3, random_state = 0)

fromsklearn.preprocessing import StandardScaler

sc_X = StandardScaler()

X_train = sc_X.fit_transform(X_train)

X_test = sc_X.transform(X_test)

fromsklearn.linear_model import LinearRegression

regressor = LinearRegression()

regressor.fit(X_train, Y_train)

Y_pred=regressor.predict(X_test)

plt.scatter(X_train, Y_train, color = 'red')

plt.plot(X_train, regressor.predict(X_train), color = 'blue')

plt.title('Salary vs Experience (Training set)')

plt.xlabel('Years of Experience')

plt.ylabel('Salary')

plt.show()

plt.scatter(X_test, Y_test, color = 'red')

plt.plot(X_train, regressor.predict(X_train), color = 'blue')

plt.title('Salary vs Experience (Test set)')

plt.xlabel('Years of Experience')

plt.ylabel('Salary')

plt.show()

Dataset:-

Y_test and Y_train :-

Variable Explorer:- 

Graph:-

Understanding of Data Pre-Processing for given dataset 1 using Spyder - ML

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

dataset = pd.read_csv('D:\SEM 7\ML\dataset_p1.csv')

X = dataset.iloc[:, :-1].values

y = dataset.iloc[:, 3].values

from sklearn.preprocessing import Imputer

imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)

imputer = imputer.fit(X[:, 1:3])

X[:, 1:3] = imputer.transform(X[:, 1:3])

from sklearn.preprocessing import Imputer

from sklearn.impute import SimpleImputer

imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)

imputer = SimpleImputer(missing_values=np.nan,strategy='mean',verbose=0)

imputer = imputer.fit(X[:, 1:3])

X[:, 1:3] = imputer.transform(X[:, 1:3])

Variable Explorer:-

Dataset:-

Output:-

Code :-

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

dataset = pd.read_csv('D:\SEM 7\ML\dataset2.csv')

X = dataset.iloc[:, :].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X = LabelEncoder()

X[:, 0] = labelencoder_X.fit_transform(X[:, 0])

#onehotencoder = OneHotEncoder(categorical_features = [0])

#X = onehotencoder.fit_transform(X).toarray()

X[:, 1] = labelencoder_X.fit_transform(X[:, 2])

#onehotencoder = OneHotEncoder(categorical_features = [1])

#X = onehotencoder.fit_transform(X).toarray()

X[:, 2] = labelencoder_X.fit_transform(X[:, 2])

onehotencoder = OneHotEncoder(categorical_features = [0])

X = onehotencoder.fit_transform(X).toarray()

onehotencoder = OneHotEncoder(categorical_features = [4])

X = onehotencoder.fit_transform(X).toarray()

onehotencoder = OneHotEncoder(categorical_features = [6])

X = onehotencoder.fit_transform(X).toarray()

Variable Explorer:-

Dataset:-

Output:-

Encryption and decryption using Hill Cipher Technique - System Security | Python3

import numpy as np
from itertools import islice, chain, repeat
list2 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',
         'w', 'x', 'y','z']
l3=[]
l4=[]
lis4=[]
def chunk_pad(it, size, padval=23):
    it = chain(iter(it), repeat(padval))
    return iter(lambda: tuple(islice(it, size)), (padval,) * size)

#Key-matrix = a
a=[[6,24,1],[13,16,10],[20,17,15]]
x = np.array(a)
p=np.matrix(a)
msg= input("Enter msg: ")
list1=list(msg)
l2=[]

for i in range(len(list1)):
    for j in range((26)):
        if(list1[i]==list2[j]):
            l2.append(j)

b=list(chunk_pad(l2, 3, 23))
for e in range(len(b)):
    list0=b[e]
    list0=list(list0)
    mul=np.dot(a,list0)
    CT=mul%26
    for i in range(len(CT)):
        for j in range(26):
            if(CT[i]==j):
                l3.append(list2[j])
                lis4.append(j)
enc=''.join(l3)
print("\nEncrypted Message: "+str(enc))


#print("\n\n====Receiver Side====")
det1=np.linalg.det(x)
c=list(chunk_pad(lis4,3,23))
adj=np.linalg.inv(x).T * np.linalg.det(x)

PT1=[]
for e in range(len(c)):
    list11=c[e]
    list11=list(list11)
    for i in range(26):
        if (i*det1)%26==1:
            det2=i
            inverse=(det2 * adj % 26).T
            mul1 = np.dot(inverse, list11)
            PT = mul1 % 26
            PT1=PT %26
            PT3=[]
            for q in range(len(PT1)):
                PT3.append(int(round(PT1[q])))

            for o in range(len(PT3)):
                if(PT3[o]==26.):
                    PT3[o]=0
            for i in range(len(PT3)):
                for j in range(26):
                    if PT3[i] == j:
                        l4.append(list2[j])
            dec=''.join(l4)
            break
        else:
            continue
print("\nDecrypted Messge: " + str(dec))

Screenshot: