Machine learning:- K-means

Why do we need Data Preprocessing?

A real-world data generally contains noises, missing values, and maybe in an unusable format that cannot be directly used for machine learning models. Data preprocessing is required tasks for cleaning the data and making it suitable for a machine learning model which also increases the accuracy and efficiency of a machine learning model.

It involves the below steps:

• Getting the dataset

• Importing libraries

• Importing datasets

• Finding Missing Data

• Encoding Categorical Data

• Splitting dataset into training and test set

• Feature scaling

---

from sklearn.impute import SimpleImputer 

import pandas as pd

import numpy as np

from sklearn.preprocessing import LabelEncoder,OneHotEncoder 

from sklearn.preprocessing import StandardScaler

from sklearn.compose import ColumnTransformer

from sklearn.model_selection import train_test_split  

from sklearn.preprocessing import StandardScaler

data_set= pd.read_csv('d:/Data.csv') 

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

#Fitting imputer object to the independent variables x.   

x= data_set.iloc[:,:-1].values 

imputer= imputer.fit(x[:, 1:3])  

#Replacing missing data with the calculated mean value  

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

print(x)

label_encoder_x_1 = LabelEncoder()

x[: , 0] = label_encoder_x_1.fit_transform(x[:,0])

transformer = ColumnTransformer(

   [('Country', OneHotEncoder(sparse=False),[0]),],remainder='passthrough'

)

x = transformer.fit_transform(x)

print(x)

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

labelencoder_y= LabelEncoder()  

y= labelencoder_y.fit_transform(y)  

print(y)

x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.2, random_state=0)

#print("Training Data Set are ",x_train,y_train)

#print("Testing Data Set are",x_test,y_test)

st_x= StandardScaler()  

x_train= st_x.fit_transform(x_train)  

x_test= st_x.transform(x_test) 

print(x_train)

print(x_test)

Machine learning:- K-means

K-means clustering is a method for finding clusters and cluster centers in a set of unlabelled data.

cluster means group of matching data ,we can show using different label for example we can create three different sub-group for red ,green and blue to manage related data ,if item will be belonging from red color then it will be the part of red cluster.

step for clustering:-

1) prepare data using repository or from array using numpy
2)  if we want to re scale data then we can use whiten()
3)  calculate centroid point from data based on number of cluster.
4)  display possible matching from cluster with values it will return the minimum difference using 0,1 and 2 ... form
Complete code of K-means Clustering algorithm ,it will be mainly implemented in ML:-
Complete example of Clustering concept

from numpy import hstack,array

from numpy.random import rand

from scipy.cluster.vq import *

data = vstack((rand(10,3) + array([1,1,1]),rand(10,3)))

centroids,_ = kmeans(data,5)

print(centroids)

clx,_ = vq(data,centroids)

print(clx)