Supervised Learning - Classification Week 8 Challenge

Question 1 :

Answer :

KNN: K-Nearest Neighbors is a supervised machine learning algorithm that classifies a data point based on how its neighbors are classified. It classifies new cases based on feature similarity measures.

KNN algorithm

k in the KNN algorithm is a parameter that refers to the number of nearest neighbors to include in the majority voting process. It is important to choose the right value of k in order to attain high model accuracy and this is achieved by parameter tuning.

KNN is used for data that is labeled, small in size, and noise-free.

KNN algorithm calculates the distance between two points using the Euclidean formula.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import plot_confusion_matrix

#Dataframe df
df = pd.read_csv('faults.csv')
#To print data
print(df)
#Size of data
print('n',df.shape)
#To print column names of data
print('n',df.columns)
#To check for datatype and presence of null value for each column
print('n',df.info())
print('n',df.isnull().values.any())

#Last seven columns are type of error classification
label_columns = df.columns.values[-7:]
#Assigning the error classification values to variable targets
targets = (df.iloc[:,-7:]==1).idxmax(1)
print('n',label_columns)
print('n',targets)

#Dropping the 7 error classification columns and retaining only targets
dataset = df.drop(label_columns,axis=1)
#creating a new column 'target' with all classification values
dataset['target']=targets
print('n',dataset)
#Printing count of each type of error
print('n',dataset.target.value_counts())

fig, ax = plt.subplots(1,2,figsize=(15,6))
#Visualising the distribution of each error using histogram and pie chart
sns.countplot(x='target',data=dataset, ax=ax[0])
dataset['target'].value_counts().plot.pie(autopct = '%.1f', ax=ax[1])

#Visualising the correlation among each dataset feature
plt.figure(figsize=(10,11))
sns.heatmap(dataset.corr(),annot=False)

#Dropping features with high correlation to others
dataset=dataset.drop('TypeOfSteel_A400',axis=1)
dataset=dataset.drop('X_Minimum',axis=1)
dataset=dataset.drop('Y_Minimum',axis=1)

#Assigning feature column values to x
x = dataset.iloc[:,0:24]
#Assingning target column values to y 
y = dataset.iloc[:,24]

#Splitting the dataset to training and testing sets
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2, random_state = 0)

#KNN model
knn = KNeighborsClassifier(n_neighbors= 19)
knn.fit(x_train,y_train)
classfier = knn.fit(x_train,y_train)
print("nKNN accuracy:",knn.score(x_test,y_test))
plot_confusion_matrix(classfier,x_test,y_test,labels=None, sample_weight=None, normalize=None)
plt.show()

Question 2 :

Answer :

Pros:

• It is a simple Machine Learning algorithm and easy to understand.
• kNN can be used for classification and regression.
• It can be used for multi-class problem.
• It provides a variety of distance criteria to choose from: Euclidean Distance Hamming Distance Manhattan Distance Minkowski Distance

Cons:

• It is difficult to choose the optimal number of neighbors to be consider while classifying the new data entry.
• K-NN inherently has no capability of dealing with missing value problem.
• k Nearest Neighbor has calculate the proximity between each neighbors for every single value in the dataset. For large daasets its computation cost increases.