# Author Archive Slide Scope

### K means clustering algorithm example using Python

K Means Clustering is an algorithm of Unsupervised Learning. You can apply this algorithm on datasets without labeled output data.Only Input data is there an we have a goal of finding regularities in data to group or cluster like items together.

You can copy the code an run it line by line in Jupyter Notebook.

Watch the videos given in the bottom of this post to understand the process clearly.

What is a Cluster – Datapoints aggregated together because of certain similarities

``` import numpy as np import matplotlib.pyplot as plt # Import the algorithm from scikitlearn https://scikit-learn.org from sklearn.cluster import KMeans # Get the dataset of wine https://archive.ics.uci.edu/ml/datasets/wine names = ['Class', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', \ 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315',\ 'Proline'] data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', names = names) data.head(100) data['Class'].value_counts().plot(kind='bar') data.plot.scatter(x = 'Alcohol', y = 'OD280/OD315', figsize=(8,5)) ``` ``` data.plot.scatter(x = 'Alcohol', y = 'OD280/OD315', c= 'Class', figsize=(8,5), colormap='jet') data.iloc[:,[12,1]].head() # kmeans = Kmeans().fit(data) # kmeans = KMeans(n_clusters = 2) # kmeans.fit(X) # kmeans.cluster_centers_ # kmeans.labels_ kmeans = KMeans(n_clusters=3, init = 'random', max_iter = 1, random_state = 5).fit(data.iloc[:,[12,1]])```
``` centroids_df = pd.DataFrame(kmeans.cluster_centers_, columns = list(data.iloc[:,[12,1]].columns.values)) fig, ax = plt.subplots(1, 1) data.plot.scatter(x = 'Alcohol', y = 'OD280/OD315', c= kmeans.labels_, figsize=(12,8), colormap='jet', ax=ax, mark_right=False) centroids_df.plot.scatter(x = 'Alcohol', y = 'OD280/OD315', ax = ax, s = 80, mark_right=False) kmeans = KMeans(n_clusters=3, init = 'random', max_iter = 150, random_state = 5).fit(data.iloc[:,[12,1]]) centroids_df = pd.DataFrame(kmeans.cluster_centers_, columns = list(data.iloc[:,[12,1]].columns.values)) fig, ax = plt.subplots(1, 1) data.plot.scatter(x = 'Alcohol', y = 'OD280/OD315', c= kmeans.labels_, figsize=(12,8), colormap='jet', ax=ax, mark_right=False) centroids_df.plot.scatter(x = 'Alcohol', y = 'OD280/OD315', ax = ax, s = 80, mark_right=False) ``` What is K means clustering ?
You can watch the theory here :

Applying K means clustering on wine dataset :

### How to plot Boxplot in Python

A box plot is used to visualize 5 values in a dataset for the selected column(s):

• Minimum Value
• First Quartile or 25%
• Median (Second Quartile) or 50%
• Third Quartile or 75%
• Maximum value

Box Plot is also known as Box and Whisker Plot.

Steps –

1. Load the dataset using Pandas dataframe
2. Select any column to visualize
3. Plot boxplot using Pandas
OR
4. Plot boxplot using Seaborn

Python Code :

import pandas as pd

index age gender bmi children smoker region charges id
0 19 female 27.900 0 yes southwest 16884.92400 1
1 18 male 33.770 1 no southeast 1725.55230 2
2 28 male 33.000 3 no southeast 4449.46200 3
3 33 male 22.705 0 no northwest 21984.47061 4
4 32 male 28.880 0 no northwest 3866.85520 5
5 31 female 25.740 0 no southeast 3756.62160 6
6 46 female 33.440 1 no southeast 8240.58960 7
7 37 female 27.740 3 no northwest 7281.50560 8
8 37 male 29.830 2 no northeast 6406.41070 9
9 60 female 25.840 0 no northwest 28923.13692 10

data.describe()

age bmi children charges id
count 1338.000000 1338.000000 1338.000000 1338.000000 1338.000000
mean 39.207025 30.663397 1.094918 13270.422265 669.500000
std 14.049960 6.098187 1.205493 12110.011237 386.391641
min 18.000000 15.960000 0.000000 1121.873900 1.000000
25% 27.000000 26.296250 0.000000 4740.287150 335.250000
50% 39.000000 30.400000 1.000000 9382.033000 669.500000
75% 51.000000 34.693750 2.000000 16639.912515 1003.750000
max 64.000000 53.130000 5.000000 63770.428010 1338.000000

# In pandas boxplot one attribute, column is required to plot boxplot
# Column can take name of one column of the dataset or the list of columns
data.boxplot(column=[‘age’],figsize=[10,7]) # We can group data as well.

data.boxplot(column=[‘age’], by=[‘gender’], figsize=[10,7])

# Boxplot Using Seaborn Library

### Magento Website ka Seo kaise kare

How to do SEO of a magento website

Go to this URL where there is a demo of magento

And follow the instructions in this video :

## मशीन लर्निंग क्या है

मशीन लर्निंग एक ऐसी तकनीक है जिसमे कंप्यूटर को इस तरह से प्रोग्राम किया जाता है की वो इनपुट डाटा के आधार पे खुद से आउटपुट डाटा को प्रेडिक्ट कर सके | दिए गए इनपुट के आधार पे खुद से सीख सके |
सॉफ्टवेयर डेवलपमेंट में जरुरत के आधार पे प्रोग्राम बनाया जाता है | मशीन लर्निंग में मशीन किसी इंसान की तरह आर्टिफिशियल इंटेलिजेंस का इस्तेमाल करके टास्क को खुद से करने की काबिलियत सीखती है |

मशीन लर्निंग आर्टिफिशियल इंटेलिजेंस विषय के अंदर आती है |

## मशीन लर्निंग के प्रकार –

• सुपरवाईस्ड लर्निंग
• अन्सुपरवाईस्ड लर्निंग
• रीइंफोर्स्मेंट लर्निंग

### Maths Module in Python Language

How to Import Maths Module in Python

Maths module is already included with python installation.

To import any module in your program you must use:

import <modulename>

To import Math module write:

>>>import math

Example

>>> print(pi)

NameError: name ‘pi’ is not defined

>>> import math

>>> print(math.pi)

3.141592653589793

>>> print(pi)

NameError: name ‘pi’ is not defined

We must use math. before calling any method or variable of math module.

>>> math.factorial(5) # To calculate factorial

120

>>> math.e

2.718281828459045

### Typecasting in Python – Changing Data Types in Python

Typecasting in Python

It is the process of changing one datatype to another data type.

Functions used to do typecasting.

str(x) – To change x to string

int(x) – To change x to integer ( x must be float or bool but not complex or string)

float(x) – To change x to float ( x must be int or boolean)

complex(x) – To change x to complex number. ( x must be numeric or bool)

bool(x) – To change values to True or False ( 1 is true and 0 is False )

Here x is the variable which has to be typecasted.

>>>X = “15” # Here x is string

>>>type(X)

<class ‘str’>

>>> print(int(X))

15 # X is now integer

### Taking User Input in Python Language

How to Take User Input in Python

We can take user input in python using the console window or we can also create GUI using python.

To create GUI windows in Python we use a library or module called Tkinter.

To take user input in python using console window we use and built in function called:

input()

Example:

name = input(“Enter Name”)

# Here name is the variable where we want to store of the value which user will enter in the console screen.

“Enter Name” is an instruction to user.

input() function creates string values only.

Example :

name = input(“Enter Name”)

if(name==”Mohan”):

print(“Welcome Mohan”)

if(pwd==”123″):

else:

print(“You are not mohan”)

Exercise : Create a program in python to calculate the area of a triangle.

a=float(input(“enter a in cm”))

b=float(input(“enter b in cm”))

c=float(input(“enter c in cm”))

s=(a+b+c)/2

print(“area is ” ,(s*(s-a)*(s-b)*(s-c))**0.5, “cm^3”)

input()

### Comparison Operators in Python Language

Comparison Operators

In programming comparison operators are used to compare two or more values. These are comparison operators used in Python.

1. Equals To – “==”

To check whether one value is equal to other value.  The answer is returned in Boolean format.

Example:

>>> name = “Mohit”  # Assignment

>>> name == “Mohit”  # Comparison

True  # Output

>>> x = 5  # Assigment

>>> 5 == x  # Comparsion – LHS is equal to RHS here

True

*Use of If Condition

Syntax of if

if(condition):

statement

Example

>>> name = “Ramesh”

>>> if(name == “Ramesh”):

#indentation block

print(“Welcome Ramesh Babu”)

Welcome Ramesh Babu  #output

1. Greater than: “ > “

>>> x = 67

>>> if(x > 65):

print(x, ” is greater than 65″)

67 is greater than 65

Example with With if and else :

x = 68

if(x > 65):

print(x , ” is g t 65 “)

else:

print(x , ” is ngt 65 “)

# 68 is g t 65

1. Greater than or equal to

Example :

>>> t = 678   # assignment

>>> t >= 789 # comparison

False              # output

>>> t >= 678

True

>>> t >= 677

True

1. Less Than

>>> cb = 56

>>> cb < 45

False

1. Less than or equal to

>>> cb = 56

>>> cb <=  56

True

>>> cb <=  78

True

### Assignment Operators in Python Language

Here are the Assignment Operators in Python Language

Assignment Operators are used to assign values to variables. In simple words, putting value in a variable. These values can be of any data type.

1. Equal to Operator – ‘=’

Equal to operator is used to assign any value to a variable.

Example –

>>> x = 5

>>> name = ‘Mohit’

Here value of 5 (integer) is assigned to variable x

Value of  Mohit (String type) is assigned to variable name.

Note – the value to be assigned (defined) should be written on right hand side of = sign.

Here x = 5  is correct but 5 = x is incorrect.

See an example:

>>> a = 65

>>> b = a

>>> print(b)

65

>>> u = 65

>>> u = v #Will raise Error

See Example

>>> x = 56

>>> x += 56

>>> print(x)

112

Here x + = 56 means x = x + 56

1. Assignment with Subtraction

>>> g = 58

>>> g -= 8    #   g = g-8 | g = 58 – 8

>>> print(g)

50

We are assigning a value of g – 8 to g itself.

1. Assignment with multiplication

>>> a = 4

>>> a *= 5   # a = a * 5

>>> print(a)

20

1. Assignment with Division

>>> df = 45

>>> df /= 5   # df = df / 5

>>> print(df)

9.0

### Arithmetic Operators in Python Language

Arithmetic Operators

1. Addition – ‘+’ – To Sum two or more numbers

>>> x = 56

>>> y = 45

>>> x + y

101

1. Subtraction – ‘-’ – To Subtract two or more numbers

>>> a = 345

>>> b = 123

>>> a – b

222

1. Multiplication – ‘*’ – To find the product of two or more numbers

>>> u = 45

>>> v = 3

>>> u * v

135

>>> a * b * u

1909575

1. Division – ‘/’ – To divide two or more numbers. Answer is always a float.

>>> people = 6

>>> quantity = 42

>>> quantity / people

7.0

1. Exponentiation – ‘**’ – To find y raised to power of x or vice versa.

>>> x = 5

>>> y = 2

>>> y ** x

32

>>> x ** y

25

1. Modulus – ‘%’ – To find the remainder in any division.

>>> x = 22

>>> y = 7

>>> x / y

3.142857142857143

>>> x % y  # Finding the remainder

1

>>> 343 % 4

3

1. To Find Quotient in Division – ‘//’

>>> 343 / 4 # Division

85.75

>>> 343 % 4 # Remainder

3

>>> 343 // 4 # Quotient

85