diff --git a/.gitignore b/.gitignore
index 96422cb2e..5f9132236 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,7 @@ __pycache__/
 *.so
 
 # Distribution / packaging
+.vscode/
 .Python
 env/
 build/
diff --git a/.vscode/settings.json b/.vscode/settings.json
deleted file mode 100644
index 615aafb03..000000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "python.pythonPath": "/usr/bin/python3"
-}
\ No newline at end of file
diff --git a/ArithmeticAnalysis/LUdecomposition.py b/ArithmeticAnalysis/LUdecomposition.py
index bb0827bb3..da05fb65d 100644
--- a/ArithmeticAnalysis/LUdecomposition.py
+++ b/ArithmeticAnalysis/LUdecomposition.py
@@ -1,7 +1,7 @@
-import math
 import numpy
 
-def LUDecompose (table): #table that contains our data
+def LUDecompose (table):
+    #table that contains our data
     #table has to be a square array so we need to check first
     rows,columns=numpy.shape(table)
     L=numpy.zeros((rows,columns))
@@ -31,4 +31,4 @@ def LUDecompose (table): #table that contains our data
 matrix =numpy.array([[2,-2,1],[0,1,2],[5,3,1]])
 L,U = LUDecompose(matrix)
 print(L)
-print(U)
\ No newline at end of file
+print(U)
diff --git a/ArithmeticAnalysis/NewtonRaphsonMethod.py b/ArithmeticAnalysis/NewtonRaphsonMethod.py
index 40501134e..5e7e2f930 100644
--- a/ArithmeticAnalysis/NewtonRaphsonMethod.py
+++ b/ArithmeticAnalysis/NewtonRaphsonMethod.py
@@ -1,18 +1,16 @@
-# Implementing Newton Raphson method in python
+# Implementing Newton Raphson method in Python
 # Author: Haseeb
 
 from sympy import diff
 from decimal import Decimal
-from math import sin, cos, exp
 
 def NewtonRaphson(func, a):
     ''' Finds root from the point 'a' onwards by Newton-Raphson method '''
     while True:
-        x = a
         c = Decimal(a) - ( Decimal(eval(func)) / Decimal(eval(str(diff(func)))) )
         
-        x = c
         a = c
+
         # This number dictates the accuracy of the answer
         if  abs(eval(func)) < 10**-15:
             return  c
@@ -20,8 +18,8 @@ def NewtonRaphson(func, a):
 
 # Let's Execute
 if __name__ == '__main__':
-    # Find root of trignometric fucntion
-    # Find value of  pi
+    # Find root of trigonometric function
+    # Find value of pi
     print ('sin(x) = 0', NewtonRaphson('sin(x)', 2))
     
     # Find root of polynomial
@@ -30,7 +28,7 @@ if __name__ == '__main__':
     # Find Square Root of 5
     print ('x**2 - 5 = 0', NewtonRaphson('x**2 - 5', 0.1))
 
-    #  Exponential Roots
+    # Exponential Roots
     print ('exp(x) - 1 = 0', NewtonRaphson('exp(x) - 1', 0))
     
     
diff --git a/Multi_Hueristic_Astar.py b/Graphs/Multi_Hueristic_Astar.py
similarity index 99%
rename from Multi_Hueristic_Astar.py
rename to Graphs/Multi_Hueristic_Astar.py
index 7fbd2ff04..1acd098f3 100644
--- a/Multi_Hueristic_Astar.py
+++ b/Graphs/Multi_Hueristic_Astar.py
@@ -1,8 +1,6 @@
 from __future__ import print_function
 import heapq
 import numpy as np
-import math
-import copy
 
 try:
     xrange          # Python 2
diff --git a/Graphs/basic-graphs.py b/Graphs/basic-graphs.py
index 6e433b5bd..28fa3d180 100644
--- a/Graphs/basic-graphs.py
+++ b/Graphs/basic-graphs.py
@@ -140,7 +140,7 @@ from collections import deque
 
 
 def topo(G, ind=None, Q=[1]):
-    if ind == None:
+    if ind is None:
         ind = [0] * (len(G) + 1) 		# SInce oth Index is ignored
         for u in G:
             for v in G[u]:
diff --git a/Maths/BasicMaths.py b/Maths/BasicMaths.py
index f05cde64d..6e8c919a0 100644
--- a/Maths/BasicMaths.py
+++ b/Maths/BasicMaths.py
@@ -62,9 +62,13 @@ def eulerPhi(n):
         s *= (x - 1)/x 
     return s   
 
-print(primeFactors(100))
-print(numberOfDivisors(100))
-print(sumOfDivisors(100))
-print(eulerPhi(100))
+def main():
+    print(primeFactors(100))
+    print(numberOfDivisors(100))
+    print(sumOfDivisors(100))
+    print(eulerPhi(100))
+    
+if __name__ == '__main__':
+    main()
     
     
\ No newline at end of file
diff --git a/Maths/FibonacciSequenceRecursion.py b/Maths/FibonacciSequenceRecursion.py
new file mode 100644
index 000000000..a97bb8b3f
--- /dev/null
+++ b/Maths/FibonacciSequenceRecursion.py
@@ -0,0 +1,18 @@
+# Fibonacci Sequence Using Recursion
+
+def recur_fibo(n):
+    return n if n <= 1 else (recur_fibo(n-1) + recur_fibo(n-2))
+    
+def isPositiveInteger(limit):
+    return limit >= 0
+
+def main():
+    limit = int(input("How many terms to include in fibonacci series: "))
+    if isPositiveInteger(limit):
+        print(f"The first {limit} terms of the fibonacci series are as follows:")
+        print([recur_fibo(n) for n in range(limit)])
+    else:
+        print("Please enter a positive integer: ")
+
+if __name__ == '__main__':
+    main()
diff --git a/Maths/GreaterCommonDivisor.py b/Maths/GreaterCommonDivisor.py
new file mode 100644
index 000000000..15adaca1f
--- /dev/null
+++ b/Maths/GreaterCommonDivisor.py
@@ -0,0 +1,15 @@
+# Greater Common Divisor - https://en.wikipedia.org/wiki/Greatest_common_divisor
+def gcd(a, b):
+    return b if a == 0 else gcd(b % a, a)
+
+def main():
+    try:
+        nums = input("Enter two Integers separated by comma (,): ").split(',')
+        num1 = int(nums[0]); num2 = int(nums[1])
+    except (IndexError, UnboundLocalError, ValueError):
+        print("Wrong Input")
+    print(f"gcd({num1}, {num2}) = {gcd(num1, num2)}")
+
+if __name__ == '__main__':
+    main()
+
diff --git a/Maths/fibonacciSeries.py b/Maths/fibonacciSeries.py
deleted file mode 100644
index cf025b07d..000000000
--- a/Maths/fibonacciSeries.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Fibonacci Sequence Using Recursion
-
-def recur_fibo(n):
-   if n <= 1:
-       return n
-   else:
-       return(recur_fibo(n-1) + recur_fibo(n-2))
-    
-limit = int(input("How many terms to include in fionacci series:"))
-
-if limit <= 0:
-   print("Plese enter a positive integer")
-else:
-   print("Fibonacci series:")
-   for i in range(limit):
-       print(recur_fibo(i))
diff --git a/Maths/gcd.py b/Maths/gcd.py
deleted file mode 100644
index 0f0ba7dce..000000000
--- a/Maths/gcd.py
+++ /dev/null
@@ -1,12 +0,0 @@
-def gcd(a, b):  
-    if a == 0 : 
-        return b  
-      
-    return gcd(b%a, a)
-
-def main():
-	print(gcd(3, 6))
-
-
-if __name__ == '__main__':
-	main()
diff --git a/Neural_Network/perceptron.py b/Neural_Network/perceptron.py
index 8ac3e8fc6..44c98e29c 100644
--- a/Neural_Network/perceptron.py
+++ b/Neural_Network/perceptron.py
@@ -25,7 +25,7 @@ class Perceptron:
         self.col_sample = len(sample[0])
         self.weight = []
 
-    def trannig(self):
+    def training(self):
         for sample in self.sample:
             sample.insert(0, self.bias)
 
@@ -121,4 +121,4 @@ while True:
     sample = []
     for i in range(3):
         sample.insert(i, float(input('value: ')))
-    network.sort(sample)
\ No newline at end of file
+    network.sort(sample)
diff --git a/Project Euler/Problem 9/sol1.py b/Project Euler/Problem 09/sol1.py
similarity index 100%
rename from Project Euler/Problem 9/sol1.py
rename to Project Euler/Problem 09/sol1.py
diff --git a/Project Euler/Problem 9/sol2.py b/Project Euler/Problem 09/sol2.py
similarity index 98%
rename from Project Euler/Problem 9/sol2.py
rename to Project Euler/Problem 09/sol2.py
index 13674d258..933f5c557 100644
--- a/Project Euler/Problem 9/sol2.py	
+++ b/Project Euler/Problem 09/sol2.py	
@@ -3,7 +3,6 @@ a^2+b^2=c^2
 Given N, Check if there exists any Pythagorean triplet for which a+b+c=N
 Find maximum possible value of product of a,b,c among all such Pythagorean triplets, If there is no such Pythagorean triplet print -1."""
 #!/bin/python3
-import sys
 
 product=-1
 d=0
diff --git a/Project Euler/Problem 15/sol1.py b/Project Euler/Problem 15/sol1.py
index 9b61b37d2..d24748011 100644
--- a/Project Euler/Problem 15/sol1.py	
+++ b/Project Euler/Problem 15/sol1.py	
@@ -1,5 +1,5 @@
 from __future__ import print_function
-from math import factorial, ceil
+from math import factorial
 
 def lattice_paths(n):
 	n = 2*n #middle entry of odd rows starting at row 3 is the solution for n = 1, 2, 3,...
diff --git a/README.md b/README.md
index 8297bbca0..f0c40802b 100644
--- a/README.md
+++ b/README.md
@@ -13,9 +13,9 @@ These are for demonstration purposes only. There are many implementations of sor
 From [Wikipedia][bubble-wiki]: Bubble sort, sometimes referred to as sinking sort, is a simple sorting algorithm that repeatedly steps through the list to be sorted, compares each pair of adjacent items and swaps them if they are in the wrong order. The pass through the list is repeated until no swaps are needed, which indicates that the list is sorted.
 
 __Properties__
-* Worst case performance	O(n^2)
+* Worst case performance	O(n<sup>2</sup>)
 * Best case performance	O(n)
-* Average case performance	O(n^2)
+* Average case performance	O(n<sup>2</sup>)
 
 ###### View the algorithm in [action][bubble-toptal]
 
@@ -26,19 +26,19 @@ __Properties__
 From [Wikipedia][bucket-wiki]: Bucket sort, or bin sort, is a sorting algorithm that works by distributing the elements of an array into a number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm, or by recursively applying the bucket sorting algorithm.
 
 __Properties__
-* Worst case performance	O(n^2)
+* Worst case performance	O(n<sup>2</sup>)
 * Best case performance	O(n+k)
 * Average case performance	O(n+k)
 
-### Coctail shaker
+### Cocktail shaker
 ![alt text][cocktail-shaker-image]
 
 From [Wikipedia][cocktail-shaker-wiki]: Cocktail shaker sort, also known as bidirectional bubble sort, cocktail sort, shaker sort (which can also refer to a variant of selection sort), ripple sort, shuffle sort, or shuttle sort, is a variation of bubble sort that is both a stable sorting algorithm and a comparison sort. The algorithm differs from a bubble sort in that it sorts in both directions on each pass through the list.
 
 __Properties__
-* Worst case performance	O(n^2)
+* Worst case performance	O(n<sup>2</sup>)
 * Best case performance	O(n)
-* Average case performance	O(n^2)
+* Average case performance	O(n<sup>2</sup>)
 
 
 ### Insertion
@@ -47,9 +47,9 @@ __Properties__
 From [Wikipedia][insertion-wiki]: Insertion sort is a simple sorting algorithm that builds the final sorted array (or list) one item at a time. It is much less efficient on large lists than more advanced algorithms such as quicksort, heapsort, or merge sort.
 
 __Properties__
-* Worst case performance	O(n^2)
+* Worst case performance	O(n<sup>2</sup>)
 * Best case performance	O(n)
-* Average case performance	O(n^2)
+* Average case performance	O(n<sup>2</sup>)
 
 ###### View the algorithm in [action][insertion-toptal]
 
@@ -73,12 +73,26 @@ __Properties__
 From [Wikipedia][quick-wiki]: Quicksort (sometimes called partition-exchange sort) is an efficient sorting algorithm, serving as a systematic method for placing the elements of an array in order.
 
 __Properties__
-* Worst case performance	O(n^2)
+* Worst case performance	O(n<sup>2</sup>)
 * Best case performance	O(n log n) or O(n) with three-way partition
 * Average case performance	O(n log n)
 
 ###### View the algorithm in [action][quick-toptal]
 
+
+### Heap
+
+From [Wikipedia][heap-wiki]: Heapsort is a comparison-based sorting algorithm. It can be thought of as an improved selection sort. It divides its input into a sorted and an unsorted region, and it iteratively shrinks the unsorted region by extracting the largest element and moving that to the sorted region
+
+__Properties__
+* Worst case performance	O(n log n)
+* Best case performance	O(n log n)
+* Average case performance	O(n log n)
+
+
+
+###### View the algorithm in [action](https://www.toptal.com/developers/sorting-algorithms/heap-sort)
+
 ### Radix
 
 From [Wikipedia][radix-wiki]: In computer science, radix sort is a non-comparative integer sorting algorithm that sorts data with integer keys by grouping keys by the individual digits which share the same significant position and value.
@@ -94,9 +108,9 @@ __Properties__
 From [Wikipedia][selection-wiki]: The algorithm divides the input list into two parts: the sublist of items already sorted, which is built up from left to right at the front (left) of the list, and the sublist of items remaining to be sorted that occupy the rest of the list. Initially, the sorted sublist is empty and the unsorted sublist is the entire input list. The algorithm proceeds by finding the smallest (or largest, depending on sorting order) element in the unsorted sublist, exchanging (swapping) it with the leftmost unsorted element (putting it in sorted order), and moving the sublist boundaries one element to the right.
 
 __Properties__
-* Worst case performance	O(n^2)
-* Best case performance	O(n^2)
-* Average case performance	O(n^2)
+* Worst case performance	O(n<sup>2</sup>)
+* Best case performance	O(n<sup>2</sup>)
+* Average case performance	O(n<sup>2</sup>)
 
 ###### View the algorithm in [action][selection-toptal]
 
@@ -151,8 +165,40 @@ __Properties__
 * Worst case performance	O(log n)
 * Best case performance	O(1)
 * Average case performance	O(log n)
-* Worst case space complexity	O(1) 
+* Worst case space complexity	O(1)
 
+## Interpolation
+Interpolation search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys (key values). It was first described by W. W. Peterson in 1957.[1] Interpolation search resembles the method by which people search a telephone directory for a name (the key value by which the book's entries are ordered): in each step the algorithm calculates where in the remaining search space the sought item might be, based on the key values at the bounds of the search space and the value of the sought key, usually via a linear interpolation. The key value actually found at this estimated position is then compared to the key value being sought. If it is not equal, then depending on the comparison, the remaining search space is reduced to the part before or after the estimated position. This method will only work if calculations on the size of differences between key values are sensible.
+
+By comparison, binary search always chooses the middle of the remaining search space, discarding one half or the other, depending on the comparison between the key found at the estimated position and the key sought — it does not require numerical values for the keys, just a total order on them. The remaining search space is reduced to the part before or after the estimated position. The linear search uses equality only as it compares elements one-by-one from the start, ignoring any sorting.
+
+On average the interpolation search makes about log(log(n)) comparisons (if the elements are uniformly distributed), where n is the number of elements to be searched. In the worst case (for instance where the numerical values of the keys increase exponentially) it can make up to O(n) comparisons.
+
+In interpolation-sequential search, interpolation is used to find an item near the one being searched for, then linear search is used to find the exact item.
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Interpolation_search)
+
+## Jump Search
+In computer science, a jump search or block search refers to a search algorithm for ordered lists. It works by first checking all items Lkm, where {\displaystyle k\in \mathbb {N} } k\in \mathbb {N}  and m is the block size, until an item is found that is larger than the search key. To find the exact position of the search key in the list a linear search is performed on the sublist L[(k-1)m, km].
+
+The optimal value of m is √n, where n is the length of the list L. Because both steps of the algorithm look at, at most, √n items the algorithm runs in O(√n) time. This is better than a linear search, but worse than a binary search. The advantage over the latter is that a jump search only needs to jump backwards once, while a binary can jump backwards up to log n times. This can be important if a jumping backwards takes significantly more time than jumping forward.
+
+The algorithm can be modified by performing multiple levels of jump search on the sublists, before finally performing the linear search. For an k-level jump search the optimum block size ml for the lth level (counting from 1) is n(k-l)/k. The modified algorithm will perform k backward jumps and runs in O(kn1/(k+1)) time.
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Jump_search)
+
+## Quick Select
+![alt text][QuickSelect-image]
+In computer science, quickselect is a selection algorithm to find the kth smallest element in an unordered list. It is related to the quicksort sorting algorithm. Like quicksort, it was developed by Tony Hoare, and thus is also known as Hoare's selection algorithm.[1] Like quicksort, it is efficient in practice and has good average-case performance, but has poor worst-case performance. Quickselect and its variants are the selection algorithms most often used in efficient real-world implementations.
+
+Quickselect uses the same overall approach as quicksort, choosing one element as a pivot and partitioning the data in two based on the pivot, accordingly as less than or greater than the pivot. However, instead of recursing into both sides, as in quicksort, quickselect only recurses into one side – the side with the element it is searching for. This reduces the average complexity from O(n log n) to O(n), with a worst case of O(n2).
+
+As with quicksort, quickselect is generally implemented as an in-place algorithm, and beyond selecting the k'th element, it also partially sorts the data. See selection algorithm for further discussion of the connection with sorting.
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Quickselect)
+
+## Tabu
+Tabu search uses a local or neighborhood search procedure to iteratively move from one potential solution {\displaystyle x} x to an improved solution {\displaystyle x'} x' in the neighborhood of {\displaystyle x} x, until some stopping criterion has been satisfied (generally, an attempt limit or a score threshold). Local search procedures often become stuck in poor-scoring areas or areas where scores plateau. In order to avoid these pitfalls and explore regions of the search space that would be left unexplored by other local search procedures, tabu search carefully explores the neighborhood of each solution as the search progresses. The solutions admitted to the new neighborhood, {\displaystyle N^{*}(x)} N^*(x), are determined through the use of memory structures. Using these memory structures, the search progresses by iteratively moving from the current solution {\displaystyle x} x to an improved solution {\displaystyle x'} x' in {\displaystyle N^{*}(x)} N^*(x).
+
+These memory structures form what is known as the tabu list, a set of rules and banned solutions used to filter which solutions will be admitted to the neighborhood {\displaystyle N^{*}(x)} N^*(x) to be explored by the search. In its simplest form, a tabu list is a short-term set of the solutions that have been visited in the recent past (less than {\displaystyle n} n iterations ago, where {\displaystyle n} n is the number of previous solutions to be stored — is also called the tabu tenure). More commonly, a tabu list consists of solutions that have changed by the process of moving from one solution to another. It is convenient, for ease of description, to understand a “solution” to be coded and represented by such attributes.
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Tabu_search)
 ----------------------------------------------------------------------------------------------------------------------
 
 ## Ciphers
@@ -168,15 +214,38 @@ The encryption step performed by a Caesar cipher is often incorporated as part o
 ### Vigenère
 The **Vigenère cipher** is a method of encrypting alphabetic text by using a series of **interwoven Caesar ciphers** based on the letters of a keyword. It is **a form of polyalphabetic substitution**.<br>
 The Vigenère cipher has been reinvented many times. The method was originally described by Giovan Battista Bellaso in his 1553 book La cifra del. Sig. Giovan Battista Bellaso; however, the scheme was later misattributed to Blaise de Vigenère in the 19th century, and is now widely known as the "Vigenère cipher".<br>
-Though the cipher is easy to understand and implement, for three centuries it resisted all attempts to break it; this earned it the description **le chiffre indéchiffrable**(French for 'the indecipherable cipher'). 
+Though the cipher is easy to understand and implement, for three centuries it resisted all attempts to break it; this earned it the description **le chiffre indéchiffrable**(French for 'the indecipherable cipher').
 Many people have tried to implement encryption schemes that are essentially Vigenère ciphers. Friedrich Kasiski was the first to publish a general method of deciphering a Vigenère cipher in 1863.
 ###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher)
 
 ### Transposition
-In cryptography, a **transposition cipher** is a method of encryption by which the positions held by units of plaintext (which are commonly characters or groups of characters) are shifted according to a regular system, so that the ciphertext constitutes a permutation of the plaintext. That is, the order of the units is changed (the plaintext is reordered).<br> 
+In cryptography, a **transposition cipher** is a method of encryption by which the positions held by units of plaintext (which are commonly characters or groups of characters) are shifted according to a regular system, so that the ciphertext constitutes a permutation of the plaintext. That is, the order of the units is changed (the plaintext is reordered).<br>
 Mathematically a bijective function is used on the characters' positions to encrypt and an inverse function to decrypt.
 ###### Source: [Wikipedia](https://en.wikipedia.org/wiki/Transposition_cipher)
 
+### RSA (Rivest–Shamir–Adleman)
+RSA (Rivest–Shamir–Adleman) is one of the first public-key cryptosystems and is widely used for secure data transmission. In such a cryptosystem, the encryption key is public and it is different from the decryption key which is kept secret (private). In RSA, this asymmetry is based on the practical difficulty of the factorization of the product of two large prime numbers, the "factoring problem". The acronym RSA is made of the initial letters of the surnames of Ron Rivest, Adi Shamir, and Leonard Adleman, who first publicly described the algorithm in 1978. Clifford Cocks, an English mathematician working for the British intelligence agency Government Communications Headquarters (GCHQ), had developed an equivalent system in 1973, but this was not declassified until 1997.[1]
+
+A user of RSA creates and then publishes a public key based on two large prime numbers, along with an auxiliary value. The prime numbers must be kept secret. Anyone can use the public key to encrypt a message, but with currently published methods, and if the public key is large enough, only someone with knowledge of the prime numbers can decode the message feasibly.[2] Breaking RSA encryption is known as the RSA problem. Whether it is as difficult as the factoring problem remains an open question.
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/RSA_(cryptosystem))
+
+## ROT13
+![alt text][ROT13-image]
+ROT13 ("rotate by 13 places", sometimes hyphenated ROT-13) is a simple letter substitution cipher that replaces a letter with the 13th letter after it, in the alphabet. ROT13 is a special case of the Caesar cipher which was developed in ancient Rome.
+
+Because there are 26 letters (2×13) in the basic Latin alphabet, ROT13 is its own inverse; that is, to undo ROT13, the same algorithm is applied, so the same action can be used for encoding and decoding. The algorithm provides virtually no cryptographic security, and is often cited as a canonical example of weak encryption.[1]
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/ROT13)
+
+## XOR
+In cryptography, the simple XOR cipher is a type of additive cipher,[1] an encryption algorithm that operates according to the principles:
+
+A {\displaystyle \oplus } \oplus  0 = A,
+A {\displaystyle \oplus } \oplus  A = 0,
+(A {\displaystyle \oplus } \oplus  B) {\displaystyle \oplus } \oplus  C = A {\displaystyle \oplus } \oplus  (B {\displaystyle \oplus } \oplus  C),
+(B {\displaystyle \oplus } \oplus  A) {\displaystyle \oplus } \oplus  A = B {\displaystyle \oplus } \oplus  0 = B,
+where {\displaystyle \oplus } \oplus  denotes the exclusive disjunction (XOR) operation. This operation is sometimes called modulus 2 addition (or subtraction, which is identical).[2] With this logic, a string of text can be encrypted by applying the bitwise XOR operator to every character using a given key. To decrypt the output, merely reapplying the XOR function with the key will remove the cipher.
+###### Source: [Wikipedia](https://en.wikipedia.org/wiki/XOR_cipher)
+
 [bubble-toptal]: https://www.toptal.com/developers/sorting-algorithms/bubble-sort
 [bubble-wiki]: https://en.wikipedia.org/wiki/Bubble_sort
 [bubble-image]: https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Bubblesort-edited-color.svg/220px-Bubblesort-edited-color.svg.png "Bubble Sort"
@@ -220,3 +289,7 @@ Mathematically a bijective function is used on the characters' positions to encr
 
 
 [caesar]: https://upload.wikimedia.org/wikipedia/commons/4/4a/Caesar_cipher_left_shift_of_3.svg
+
+[ROT13-image]: https://en.wikipedia.org/wiki/File:ROT13_table_with_example.svg
+
+[QuickSelect-image]: https://en.wikipedia.org/wiki/File:Selecting_quickselect_frames.gif
diff --git a/ciphers/affine_cipher.py b/ciphers/affine_cipher.py
index 6c1ba06f6..4fbe87d42 100644
--- a/ciphers/affine_cipher.py
+++ b/ciphers/affine_cipher.py
@@ -68,8 +68,8 @@ def getRandomKey():
     while True:
         keyA = random.randint(2, len(SYMBOLS))
         keyB = random.randint(2, len(SYMBOLS))
-    if cryptoMath.gcd(keyA, len(SYMBOLS)) == 1:
-        return keyA * len(SYMBOLS) + keyB
+        if cryptoMath.gcd(keyA, len(SYMBOLS)) == 1:
+            return keyA * len(SYMBOLS) + keyB
 
 if __name__ == '__main__':
     import doctest
diff --git a/data_structures/LinkedList/DoublyLinkedList.py b/data_structures/LinkedList/DoublyLinkedList.py
index 6f17b7c81..18eb63fea 100644
--- a/data_structures/LinkedList/DoublyLinkedList.py
+++ b/data_structures/LinkedList/DoublyLinkedList.py
@@ -24,7 +24,7 @@ class LinkedList:
         temp = self.head
         self.head = self.head.next # oldHead <--> 2ndElement(head) 
         self.head.previous = None # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed
-        if(self.head == None):
+        if(self.head is None):
             self.tail = None
         return temp
     
@@ -58,7 +58,7 @@ class LinkedList:
             current.next.previous = current.previous # 1 <--> 3
     
     def isEmpty(self): #Will return True if the list is empty
-        return(self.head == None)
+        return(self.head is None)
         
     def display(self): #Prints contents of the list
         current = self.head
diff --git a/data_structures/LinkedList/__init__.py b/data_structures/LinkedList/__init__.py
index 1d220599f..6d50f23c1 100644
--- a/data_structures/LinkedList/__init__.py
+++ b/data_structures/LinkedList/__init__.py
@@ -19,4 +19,4 @@ class LinkedList:
             return item
 
     def is_empty(self):
-        return self.head == None
+        return self.head is None
diff --git a/data_structures/LinkedList/singly_LinkedList.py b/data_structures/LinkedList/singly_LinkedList.py
index eb7f48f17..0d6157e3e 100644
--- a/data_structures/LinkedList/singly_LinkedList.py
+++ b/data_structures/LinkedList/singly_LinkedList.py
@@ -67,3 +67,4 @@ class Linked_List:
             current = next_node
         # Return prev in order to put the head at the end
         Head = prev
+        return Head
diff --git a/dynamic_programming/k_means_clustering_tensorflow.py b/dynamic_programming/k_means_clustering_tensorflow.py
index ad495c71a..b6813c6a2 100644
--- a/dynamic_programming/k_means_clustering_tensorflow.py
+++ b/dynamic_programming/k_means_clustering_tensorflow.py
@@ -1,5 +1,5 @@
 import tensorflow as tf
-from random import choice, shuffle
+from random import shuffle
 from numpy import array
 
 
diff --git a/linear-algebra-python/src/lib.pyc b/linear-algebra-python/src/lib.pyc
deleted file mode 100644
index 7aeca0e1c..000000000
Binary files a/linear-algebra-python/src/lib.pyc and /dev/null differ
diff --git a/machine_learning/Reuters - OneVsRestClassifier.ipynb b/machine_learning/Reuters - OneVsRestClassifier.ipynb
new file mode 100644
index 000000000..968130a60
--- /dev/null
+++ b/machine_learning/Reuters - OneVsRestClassifier.ipynb	
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import nltk\n",
+    "except ModuleNotFoundError:\n",
+    "    !pip install nltk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## This code downloads the required packages.\n",
+    "## You can run `nltk.download('all')` to download everything.\n",
+    "\n",
+    "nltk_packages = [\n",
+    "    (\"reuters\", \"corpora/reuters.zip\")\n",
+    "]\n",
+    "\n",
+    "for pid, fid in nltk_packages:\n",
+    "    try:\n",
+    "        nltk.data.find(fid)\n",
+    "    except LookupError:\n",
+    "        nltk.download(pid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import reuters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up train/test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_documents, train_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('training/')])\n",
+    "test_documents, test_categories = zip(*[(reuters.raw(i), reuters.categories(i)) for i in reuters.fileids() if i.startswith('test/')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_categories = sorted(list(set(reuters.categories())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell defines a function **tokenize** that performs following actions:\n",
+    "- Receive a document as an argument to the function\n",
+    "- Tokenize the document using `nltk.word_tokenize()`\n",
+    "- Use `PorterStemmer` provided by the `nltk` to remove morphological affixes from each token\n",
+    "- Append stemmed token to an already defined list `stems`\n",
+    "- Return the list `stems`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.stem.porter import PorterStemmer\n",
+    "def tokenize(text):\n",
+    "    tokens = nltk.word_tokenize(text)\n",
+    "    stems = []\n",
+    "    for item in tokens:\n",
+    "        stems.append(PorterStemmer().stem(item))\n",
+    "    return stems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To begin, I first used TF-IDF for feature selection on both train as well as test data using `TfidfVectorizer`.\n",
+    "\n",
+    "But first, What `TfidfVectorizer` actually does?\n",
+    "- `TfidfVectorizer` converts a collection of raw documents to a matrix of **TF-IDF** features.\n",
+    "\n",
+    "**TF-IDF**?\n",
+    "- TFIDF (abbreviation of the term *frequency–inverse document frequency*) is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. [tf–idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)\n",
+    "\n",
+    "**Why `TfidfVectorizer`**?\n",
+    "- `TfidfVectorizer` scale down the impact of tokens that occur very frequently (e.g., “a”, “the”, and “of”) in a given corpus. [Feature Extraction and Transformation](https://spark.apache.org/docs/latest/mllib-feature-extraction.html#tf-idf)\n",
+    "\n",
+    "I gave following two arguments to `TfidfVectorizer`:\n",
+    "- tokenizer: `tokenize` function\n",
+    "- stop_words\n",
+    "\n",
+    "Then I used `fit_transform` and `transform` on the train and test documents repectively.\n",
+    "\n",
+    "**Why `fit_transform` for training data while `transform` for test data**?\n",
+    "\n",
+    "To avoid data leakage during cross-validation, imputer computes the statistic on the train data during the `fit`, **stores it** and uses the same on the test data, during the `transform`. This also prevents the test data from appearing in `fit` operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "vectorizer = TfidfVectorizer(tokenizer = tokenize, stop_words = 'english')\n",
+    "\n",
+    "vectorised_train_documents = vectorizer.fit_transform(train_documents)\n",
+    "vectorised_test_documents = vectorizer.transform(test_documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the **efficient implementation** of machine learning algorithms, many machine learning algorithms **requires all input variables and output variables to be numeric**. This means that categorical data must be converted to a numerical form.\n",
+    "\n",
+    "For this purpose, I used `MultiLabelBinarizer` from `sklearn.preprocessing`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "mlb = MultiLabelBinarizer()\n",
+    "train_labels = mlb.fit_transform(train_categories)\n",
+    "test_labels = mlb.transform(test_categories)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, To **train** the classifier, I used `LinearSVC` in combination with the `OneVsRestClassifier` function in the scikit-learn package.\n",
+    "\n",
+    "The strategy of `OneVsRestClassifier` is of **fitting one classifier per label** and the `OneVsRestClassifier` can efficiently do this task and also outputs are easy to interpret. Since each label is represented by **one and only one classifier**, it is possible to gain knowledge about the label by inspecting its corresponding classifier. [OneVsRestClassifier](http://scikit-learn.org/stable/modules/multiclass.html#one-vs-the-rest)\n",
+    "\n",
+    "The reason I combined `LinearSVC` with `OneVsRestClassifier` is because `LinearSVC` supports **Multi-class**, while we want to perform **Multi-label** classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "from sklearn.multiclass import OneVsRestClassifier\n",
+    "from sklearn.svm import LinearSVC\n",
+    "\n",
+    "classifier = OneVsRestClassifier(LinearSVC())\n",
+    "classifier.fit(vectorised_train_documents, train_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After fitting the classifier, I decided to use `cross_val_score` to **measure score** of the classifier by **cross validation** on the training data. But the only problem was, I wanted to **shuffle** data to use with `cross_val_score`, but it does not support shuffle argument.\n",
+    "\n",
+    "So, I decided to use `KFold` with `cross_val_score` as `KFold` supports shuffling the data.\n",
+    "\n",
+    "I also enabled `random_state`, because `random_state` will guarantee the same output in each run. By setting the `random_state`, it is guaranteed that the pseudorandom number generator will generate the same sequence of random integers each time, which in turn will affect the split.\n",
+    "\n",
+    "Why **42**?\n",
+    "- [Why '42' is the preferred number when indicating something random?](https://softwareengineering.stackexchange.com/questions/507/why-42-is-the-preferred-number-when-indicating-something-random)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "from sklearn.model_selection import KFold, cross_val_score\n",
+    "\n",
+    "kf = KFold(n_splits=10, random_state = 42, shuffle = True)\n",
+    "scores = cross_val_score(classifier, vectorised_train_documents, train_labels, cv = kf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross-validation scores: [0.83655084 0.86743887 0.8043758  0.83011583 0.83655084 0.81724582\n",
+      " 0.82754183 0.8030888  0.80694981 0.82731959]\n",
+      "Cross-validation accuracy: 0.8257 (+/- 0.0368)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Cross-validation scores:', scores)\n",
+    "print('Cross-validation accuracy: {:.4f} (+/- {:.4f})'.format(scores.mean(), scores.std() * 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the end, I used different methods (`accuracy_score`, `precision_score`, `recall_score`, `f1_score` and `confusion_matrix`) provided by scikit-learn **to evaluate** the classifier. (both *Macro-* and *Micro-averages*)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
+    "\n",
+    "predictions = classifier.predict(vectorised_test_documents)\n",
+    "\n",
+    "accuracy = accuracy_score(test_labels, predictions)\n",
+    "\n",
+    "macro_precision = precision_score(test_labels, predictions, average='macro')\n",
+    "macro_recall = recall_score(test_labels, predictions, average='macro')\n",
+    "macro_f1 = f1_score(test_labels, predictions, average='macro')\n",
+    "\n",
+    "micro_precision = precision_score(test_labels, predictions, average='micro')\n",
+    "micro_recall = recall_score(test_labels, predictions, average='micro')\n",
+    "micro_f1 = f1_score(test_labels, predictions, average='micro')\n",
+    "\n",
+    "cm = confusion_matrix(test_labels.argmax(axis = 1), predictions.argmax(axis = 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8099\n",
+      "Precision:\n",
+      "- Macro: 0.6076\n",
+      "- Micro: 0.9471\n",
+      "Recall:\n",
+      "- Macro: 0.3708\n",
+      "- Micro: 0.7981\n",
+      "F1-measure:\n",
+      "- Macro: 0.4410\n",
+      "- Micro: 0.8662\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Accuracy: {:.4f}\\nPrecision:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\\nRecall:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\\nF1-measure:\\n- Macro: {:.4f}\\n- Micro: {:.4f}\".format(accuracy, macro_precision, micro_precision, macro_recall, micro_recall, macro_f1, micro_f1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In below cell, I used `matplotlib.pyplot` to **plot the confusion matrix** (of first *few results only* to keep the readings readable) using `heatmap` of `seaborn`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x24d8cf39f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sb\n",
+    "import pandas as pd\n",
+    "\n",
+    "cm_plt = pd.DataFrame(cm[:73])\n",
+    "\n",
+    "plt.figure(figsize = (25, 25))\n",
+    "ax = plt.axes()\n",
+    "\n",
+    "sb.heatmap(cm_plt, annot=True)\n",
+    "\n",
+    "ax.xaxis.set_ticks_position('top')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, I took the data from [Coconut - Wikipedia](https://en.wikipedia.org/wiki/Coconut) to check if the classifier is able to **correctly** predict the label(s) or not.\n",
+    "\n",
+    "And here is the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Example labels: [('coconut', 'oilseed')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_text = '''The coconut tree (Cocos nucifera) is a member of the family Arecaceae (palm family) and the only species of the genus Cocos.\n",
+    "The term coconut can refer to the whole coconut palm or the seed, or the fruit, which, botanically, is a drupe, not a nut.\n",
+    "The spelling cocoanut is an archaic form of the word.\n",
+    "The term is derived from the 16th-century Portuguese and Spanish word coco meaning \"head\" or \"skull\", from the three indentations on the coconut shell that resemble facial features.\n",
+    "Coconuts are known for their versatility ranging from food to cosmetics.\n",
+    "They form a regular part of the diets of many people in the tropics and subtropics.\n",
+    "Coconuts are distinct from other fruits for their endosperm containing a large quantity of water (also called \"milk\"), and when immature, may be harvested for the potable coconut water.\n",
+    "When mature, they can be used as seed nuts or processed for oil, charcoal from the hard shell, and coir from the fibrous husk.\n",
+    "When dried, the coconut flesh is called copra.\n",
+    "The oil and milk derived from it are commonly used in cooking and frying, as well as in soaps and cosmetics.\n",
+    "The husks and leaves can be used as material to make a variety of products for furnishing and decorating.\n",
+    "The coconut also has cultural and religious significance in certain societies, particularly in India, where it is used in Hindu rituals.'''\n",
+    "\n",
+    "example_preds = classifier.predict(vectorizer.transform([example_text]))\n",
+    "example_labels = mlb.inverse_transform(example_preds)\n",
+    "print(\"Example labels: {}\".format(example_labels))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/machine_learning/linear_regression.py b/machine_learning/linear_regression.py
index eb1f019c5..8c23f1f77 100644
--- a/machine_learning/linear_regression.py
+++ b/machine_learning/linear_regression.py
@@ -59,7 +59,6 @@ def sum_of_square_error(data_x, data_y, len_data, theta):
     :param theta     : contains the feature vector
     :return          : sum of square error computed from given feature's
     """
-    error = 0.0
     prod = np.dot(theta, data_x.transpose())
     prod -= data_y.transpose()
     sum_elem = np.sum(np.square(prod))
diff --git a/other/game_of_life/game_o_life.py b/other/game_of_life/game_o_life.py
index 32ebe0fc1..1fdaa21b4 100644
--- a/other/game_of_life/game_o_life.py
+++ b/other/game_of_life/game_o_life.py
@@ -28,9 +28,8 @@ Game-Of-Life Rules:
  comes a live cell, as if by reproduction.
  '''
 import numpy as np
-import random, time, sys
+import random, sys
 from matplotlib import pyplot as plt
-import matplotlib.animation as animation
 from matplotlib.colors import ListedColormap
 
 usage_doc='Usage of script: script_nama <size_of_canvas:int>'
diff --git a/other/primelib.py b/other/primelib.py
index 16c44a093..19572f861 100644
--- a/other/primelib.py
+++ b/other/primelib.py
@@ -290,7 +290,7 @@ def goldbach(number):
     
     while (i < lenPN and loop):
         
-        j = i+1;
+        j = i+1
         
         
         while (j < lenPN and loop):
@@ -300,9 +300,8 @@ def goldbach(number):
                 ans.append(primeNumbers[i])
                 ans.append(primeNumbers[j])
                 
-            j += 1;
-            
-            
+            j += 1
+
         i += 1
         
     # precondition
diff --git a/searches/interpolation_search.py b/searches/interpolation_search.py
index 7b765c454..db9893bdb 100644
--- a/searches/interpolation_search.py
+++ b/searches/interpolation_search.py
@@ -2,7 +2,6 @@
 This is pure python implementation of interpolation search algorithm
 """
 from __future__ import print_function
-import bisect
 
 try:
     raw_input          # Python 2
diff --git a/searches/quick_select.py b/searches/quick_select.py
index e5e2ce99c..1596cf040 100644
--- a/searches/quick_select.py
+++ b/searches/quick_select.py
@@ -1,8 +1,5 @@
-import collections
-import sys
 import random
-import time
-import math
+
 """
 A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted
 https://en.wikipedia.org/wiki/Quickselect
@@ -25,23 +22,23 @@ def _partition(data, pivot):
             equal.append(element)
     return less, equal, greater
     
-    def quickSelect(list, k):
+def quickSelect(list, k):
     #k = len(list) // 2 when trying to find the median (index that value would be when list is sorted)
-      smaller = []
-      larger = []
-      pivot = random.randint(0, len(list) - 1)
-      pivot = list[pivot]
-      count = 0
-      smaller, equal, larger =_partition(list, pivot)
-      count = len(equal)
-      m = len(smaller)
+    smaller = []
+    larger = []
+    pivot = random.randint(0, len(list) - 1)
+    pivot = list[pivot]
+    count = 0
+    smaller, equal, larger =_partition(list, pivot)
+    count = len(equal)
+    m = len(smaller)
 
-      #k is the pivot
-      if m <= k < m + count:
+    #k is the pivot
+    if m <= k < m + count:
         return pivot
     # must be in smaller
-      elif m > k:
+    elif m > k:
         return quickSelect(smaller, k)
     #must be in larger
-      else:
+    else:
         return quickSelect(larger, k - (m + count))
diff --git a/sorts/bubble_sort.py b/sorts/bubble_sort.py
index fc1f9e537..9ff987fa9 100644
--- a/sorts/bubble_sort.py
+++ b/sorts/bubble_sort.py
@@ -1,15 +1,3 @@
-"""
-This is pure python implementation of bubble sort algorithm
-
-For doctests run following command:
-python -m doctest -v bubble_sort.py
-or
-python3 -m doctest -v bubble_sort.py
-
-For manual testing run:
-python bubble_sort.py
-"""
-
 from __future__ import print_function
 
 
@@ -46,7 +34,6 @@ if __name__ == '__main__':
         raw_input          # Python 2
     except NameError:
         raw_input = input  # Python 3
-
-    user_input = raw_input('Enter numbers separated by a comma:\n').strip()
+    user_input = raw_input('Enter numbers separated by a comma:').strip()
     unsorted = [int(item) for item in user_input.split(',')]
-    print(bubble_sort(unsorted))
+    print(*bubble_sort(unsorted), sep=',')
diff --git a/sorts/external-sort.py b/sorts/external-sort.py
index 6c4adc94c..dece32d48 100644
--- a/sorts/external-sort.py
+++ b/sorts/external-sort.py
@@ -4,7 +4,6 @@
 # Sort large text files in a minimum amount of memory
 #
 import os
-import sys
 import argparse
 
 class FileSplitter(object):
diff --git a/sorts/random_normaldistribution_quicksort.py b/sorts/random_normaldistribution_quicksort.py
index bd730b3b1..dfa37da61 100644
--- a/sorts/random_normaldistribution_quicksort.py
+++ b/sorts/random_normaldistribution_quicksort.py
@@ -2,7 +2,6 @@ from __future__ import print_function
 from random import randint
 from tempfile import TemporaryFile
 import numpy as np
-import math
 
 
 
diff --git a/sorts/tree_sort.py b/sorts/tree_sort.py
index 94cf68033..f8ecf84c6 100644
--- a/sorts/tree_sort.py
+++ b/sorts/tree_sort.py
@@ -11,12 +11,12 @@ class node():
     def insert(self,val):
         if self.val:
             if val < self.val:
-                if self.left == None:
+                if self.left is None:
                     self.left = node(val)
                 else:
                     self.left.insert(val)
             elif val > self.val:
-                if self.right == None:
+                if self.right is None:
                     self.right = node(val)
                 else:
                     self.right.insert(val)
diff --git a/strings/min-cost-string-conversion.py b/strings/min-cost-string-conversion.py
index 7e3298137..c233af1c8 100644
--- a/strings/min-cost-string-conversion.py
+++ b/strings/min-cost-string-conversion.py
@@ -68,7 +68,6 @@ def assemble_transformation(ops, i, j):
 			return seq
 
 if __name__ == '__main__':
-	from time import sleep
 	_, operations = compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)
 
 	m = len(operations)