ML | Implementing the KNN classifier using Sklearn

K-Nearest Neighbors is one of the most basic yet important classification algorithms in machine learning. It belongs to a supervised learning field and finds wide application in pattern recognition, data mining, and intrusion detection. It is widely available in real-world scenarios because it is nonparametric, meaning it does not make any basic assumptions about the distribution of the data (unlike other algorithms like GMM, which assume a Gaussian distribution of the data).

this article will demonstrate how to implement the Nearest Neighbor Classifier algorithm, using Sklearn library from Python.

Step 1: Import required libraries






  import   numpy as np 
  import   pandas as pd 
  from   sklearn.model_selection   import   train_test_split 

  from   sklearn.neighbors   import   KNeighborsClassifier 
  import   matplotlib.pyplot as plt 
  import   seaborn as sns

Step 2: Read the dataset

Step 3: Train the model

cd C: UsersDevDesktopKaggleBreast_Cancer
# Change the location of the read file to the file location

df = pd.read_csv ( `data.csv` )

y = df [ ` diagnosis` ]

X = df.drop ( ` diagnosis` , axis = 1 )


  X   =   X.drop (  `Unnamed: 32`  , axis   =   1  ) 
  X   =   X.drop (  `id`  , axis   =   1  ) 

  # Separate dependent and independent  
  
  X_train, X_test, y_train, y_test   =   train_test_split (
   X, y, test_size   =   0.3  , random_state   =   0   ) 
  # Splitting data into training and testing data

K = []


  training   =   [] 
  test   =   [] 
  scores   =   { } 
  
  for   k   in   range   (  2  ,   21  ):  
   clf   =   KNeighborsClassifier (n_neighbors   =   k) 
    clf.fit (X_train, y_train) 
    
   training_s core   =   clf.score (X_train, y_train) 
    test_score   =   clf.score (X_test, y_test) 
   K.append (k) 
  
   training.append (training_score)  
   test.append (test_score) 
    scores [k]   =   [training_score, test_score]

Step 4: Model Evaluation






  for   keys, values   in   scores.items ():  
   print   (keys,   `:`  , values)

Now let`s try to find the optimal value for "k", that is, the number of nearest neighbors .

Step 5: Graph learning and test results

ax = sns.stripplot (K, training);

ax. set (xlabel = `values of k` , ylabel = `Training Score` )

plt.show ()
# plot show function

ax = sns. stripplot (K, test);

ax. set (xlabel = `values of k` , ylabel = `Test Score` )

plt.show ()

plt.scatter (K, training, color = `k` )

plt.scatter (K, test, color = `g` )

plt.show ()
# For overlapping dot diagrams amm

From the above scatter plot we can conclude that the optimal k value would be around 5.

ML | Implementing the KNN classifier using Sklearn

Submit new EBook