December 19, 2018 by akhilendra

How to do KNN with Python & Sci-Kit Learn

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KNN or K-nearest neighbor is one of the easiest and most popular machine learning algorithm available to data scientists and machine learning enthusiasts.

In this post, we are going to implement KNN model with python and sci-kit learn library.

You can also implement KNN in R but that is beyond the scope for this post.

In this post, I am not going to discuss under the hood concepts of KNN and will only demonstrate the implementation.

If you want to learn more about the KNN, you can visit here.

Also, if you want to learn more about sci-kit learn librarywhich I am using here, click here.

Further, if you are interested in implementation of logisticregression using Azure ML studio, click here.

Alright, so let’s start with KNN implementation in python and with sci-kit learn library.

KNN with Python & Sci-Kit Learn

You can download the dataset from https://www.kaggle.com/ntnu-testimon/paysim1

Let’s import few libraries which are must for any machine learning algorithm in python and jupyter notebook.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Let’s load the dataset using pandas

pay_data = pd.read_csv("PS_20174392719_1491204439457_log.csv")

Now let’s review the head to verify the dataset.

pay_data.head()

pay_data.shape

(6362620, 11)

There are 6362620 row, that is observation and 11 variables, i.e. columns.

let’s drop unnecessary columns.

pay_data = .pay_data.drop(['nameOrig', 'nameDest', 'isFlaggedFraud'], axis = 1)

Let’s divide our data set into two parts;

pay_data_fraud = pay_data[pay_data['isFraud'] == 1]
pay_data_nofraud = pay_data[pay_data['isFraud'] == 0]
pay_data_fraud.head()

1. Dataset where isFraud == 1. We will call it pay_data_fraud.
2. Dataset where isFraud == 0. We will call it pay_data_nofraud.

pay_data_nofraud.head()

Let’s find out the dimension of the fraud & No Fraud data set.

pay_data_fraud.shape

(8213, 8)

pay_data_nofraud.shape

(6354407, 8)

This is a huge data set which will take lot of time for any model.Therefore let’s reduce it to so that it’s take optimum time for KNN.We have over 8000 values in fraud and so let’s take twice of those from no fraud data set.

pay_data_nofraud = pay_data_nofraud[0:16000]

Let’s join both of these data sets to build the data set which will be used for running KNN model.

pay_data_nofraud_updated = pd.concat([pay_data_fraud, pay_data_nofraud], axis = 0)

Let’s draw few charts for better understanding the data.

We are going to use seaborn and matplot lib in this analysis.

sns.relplot(x="type", y="amount", data=pay_data_nofraud_updated)

sns.relplot(x="type", y="amount",kind="line", data=pay_data_nofraud_updated)

sns.relplot(x="type", y="amount",kind="line", hue="isFraud", data=pay_data_nofraud_updated)

sns.relplot(x="type", y="amount", hue="isFraud", data=pay_data_nofraud_updated)

sns.catplot(x="type", y="amount", data=pay_data_nofraud_updated);

With scikit-learn library,  you cannot implement the machine learning algorithms on categorical columns. Therefore we cannot implement KNN on categorical columns in our data set. We will need to use label encoding and hot encoding in order to resolve this issue.

But first, we need to import label encoder and hot encoder modules in our session.

from sklearn.preprocessing import LabelEncoder 
from sklearn.preprocessing import OneHotEncoder

pay_data_nofraud_updated['type'] = pay_data_nofraud_updated['type'].astype('category')

#Integer Encoding the 'type' column
type_encode = LabelEncoder()
#Integer encoding the 'type' column
pay_data_nofraud_updated['type'] = type_encode.fit_transform(pay_data_nofraud_updated.type) 

#One hot encoding the 'type' column

type_one_hot = OneHotEncoder()
type_one_hot_encode = type_one_hot.fit_transform(pay_data_nofraud_updated.type.values.reshape(-1,1)).toarray()

#Adding the one hot encoded variables to the dataset
ohe_variable = pd.DataFrame(type_one_hot_encode, columns = ['type_'+str(int(i)) for i in range(type_one_hot_encode.shape[1])])

pay_data_nofraud_updated = pd.concat([pay_data_nofraud_updated, ohe_variable], axis=1)

#Dropping the original type variable
pay_data_nofraud_updated = pay_data_nofraud_updated.drop('type', axis = 1)

pay_data_nofraud_updated.head()

let’s check the data set for missing values.

pay_data_nofraud_updated.isnull().any()

step              True
amount            True
oldbalanceOrg     True
newbalanceOrig    True
oldbalanceDest    True
newbalanceDest    True
isFraud           True
type_0            True
type_1            True
type_2            True
type_3            True
type_4            True
dtype: bool

There are missing values in all of the above column with True. For this analysis, we will simply replace the missing value with 0 but you can explore more ideas.

pay_data_nofraud_updated = pay_data_nofraud_updated.fillna(0) 

In this KNN model;

• dependent variable is isFraud
• It contain 0 and 1
• Remaining columns/variables are independent variables
• Creating ind_var for independent variable & dep_var for dependent variable.

ind_var = pay_data_nofraud_updated.drop('isFraud', axis = 1).values 
dep_var = pay_data_nofraud_updated['isFraud'].values 
print(ind_var)
[[1.00000000e+00 9.83964000e+03 1.70136000e+05 ... 0.00000000e+00
  0.00000000e+00 1.00000000e+00]
 [1.00000000e+00 1.86428000e+03 2.12490000e+04 ... 0.00000000e+00
  0.00000000e+00 0.00000000e+00]
 [1.00000000e+00 1.81000000e+02 1.81000000e+02 ... 0.00000000e+00
  0.00000000e+00 1.00000000e+00]
 ...
 [7.43000000e+02 6.31140928e+06 6.31140928e+06 ... 0.00000000e+00
  0.00000000e+00 0.00000000e+00]
 [7.43000000e+02 8.50002520e+05 8.50002520e+05 ... 0.00000000e+00
  0.00000000e+00 0.00000000e+00]
 [7.43000000e+02 8.50002520e+05 8.50002520e+05 ... 0.00000000e+00
  0.00000000e+00 0.00000000e+00]]

print(dep_var)
[0. 0. 1. ... 1. 1. 1.]

Let’s import split model from sklearn and create training & testing data sets.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(ind_var, dep_var, test_size = 0.3, random_state = 42, stratify = dep_var)

from sklearn.neighbors import KNeighborsClassifier
#Initializing the KNN classifier with 3 neighbors
knn_class = KNeighborsClassifier(n_neighbors=3)
#Fitting the classifier on the training data
knn_class.fit(X_train, y_train)
#Extracting the accuracy score from the test sets
knn_class.score(X_test, y_test)

0.9859851607584501

import numpy as np 
from sklearn.model_selection import GridSearchCV
#grid with 1 to 24 neighbours
grid = {'n_neighbors' : np.arange(1, 25)}
#Initializing KNN classifier
knn_classif = KNeighborsClassifier()
#cross validation
knn = GridSearchCV(knn_classif, grid, cv = 10)
knn.fit(X_train, y_train)
#Extracting best parameter
knn.best_params_
#Extracting the accuracy score for optimal number of neighbors
knn.best_score_ 

0.9842763128837065

#standardization
from sklearn.preprocessing import StandardScaler 
from sklearn.pipeline import Pipeline
#Setting up the scaling pipeline
pipe = [('scaler', StandardScaler()), ('knn', KNeighborsClassifier(n_neighbors = 1))]
pipe_ord = Pipeline(pipe)
#Fitting the classfier to the scaled dataset
knn_classifier_scaled = pipe_ord.fit(X_train, y_train)
#Extracting the score
knn_classifier_scaled.score(X_test, y_test) 

0.9965993404781534

KNN Model Summary

This KNN model is exhibiting high accuracy but this is a very basic model. If you are looking to further explore KNN, i will suggest you to use different techniques to handle missing values and try different parameters in the model.

Please leave your comment and let me know your feedback. 

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#business analytics#data science#machine learning#python#scikit-learn