Supervised Learning - Classification Week 8 Challenge

2.

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.

1.

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()