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Implement Machine Learning Life Cycle:-

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=[:, 1:3])  
#Replacing missing data with the calculated mean value  
x[:, 1:3]= imputer.transform(x[:, 1:3])  

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)
y= data_set.iloc[:,3].values
labelencoder_y= LabelEncoder()  
y= labelencoder_y.fit_transform(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) 


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)
clx,_ = vq(data,centroids)


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