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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
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Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
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Heart Disease Prediction using KNN -The K-Nearest Neighbours Algorithm

Siddharth
Siddharth Last Updated : 10 Jul, 2021
5 min read

This article was published as a part of the Data Science Blogathon

Introduction:

In Machine Learning, one of the main types of learning includes Supervised Learning. Where we already have the correct output and set of features associated with that output. We use some algorithms and try to train them with the existing data and then try to predict the output of new data with only features associated with them. This is like a teacher, where the teacher teaches students about something and tells them what is correct and then when they give exams they need to know what they have learnt and provide with correct answers. KNN is used for both classifications as well as regression tasks in Machine learning.

About KNN:

  1. KNN tries to find similarities between predictors and values that are within the dataset.
  2. KNN uses a non-parametric method as there is not a particular finding of parameters to a particular functional form.
  3. It does not make any type of assumptions about the features and output of the dataset.
  4. KNN is also called a lazy classifier as it memorizes the training data and not exactly learn and fix the weights. Hence most of the computing work occurs during the classification rather than training time.
  5. KNN usually works by just trying to see to which class is the new feature near to and it just puts it to the class closest to that point.

Working of KNN Algorithm:

  1. Initially, we select a value for K in our KNN algorithm.
  2. Now we go for a distance measure. Let’s consider Eucleadean distance here. Find the euclidean distance of k neighbours.
  3. Now we check all the neighbours to the new point we have given and see which is nearest to our point. We only check for k-nearest here.
  4. Now we see to which class there is the highest number obtained. The max number is chosen and we assign our new point to that class.
  5. In this way, we use the KNN algorithm.

The ideal value of K in KNN:

Here we usually go for an odd number of K as it’s better during voting to see to which numbered class has more votes given and thus we can assign our new class to that.

If we go for too small a value of k, there is a good chance we may have overfitting of data, that’s is the algorithm may perform reasonably well of training but not well on testing data. And, we also may encounter noise if we just use the small value of k, if we have large data.

One way to determine k is to see the error plot for k and run a loop to a set of values, the k associated with the lowest error can be used for our problem. I will be performing these steps during our implementation of Heart disease data.

Pros and Cons of KNN algorithm:

Pros:

  1. We can implement the algorithm with ease.
  2. It is very effective against noisy data by averaging k-nearest neighbours.
  3. Works well in case of large data.
  4. The decision boundaries that are formed can be of arbitrary shapes.

Cons:

  1. Curse of dimensionality: Domination of distances by irrelevant attributes.
  2. Finding the correct value of k may be time expensive sometimes.
  3. Very high computation cost due to its distance measure.

Implementation of K-Nearest Neighbour on Heart disease dataset.

I have used the Heart disease UCI dataset for this task, which is available here:

1. Importing all Libraries:

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

We can see here we have imported KNeighorsClassifier for our classification task. We import this from sklearn library. Sklearn has almost all the machine learning classifiers defined and we can call them and use them for our problem.

2.  Read the heart disease dataset:

df = pd.read_csv('heart.csv')
df.head()
data | heart disease prediction KNN

As we can see target tells us if the person is suffering from heart disease or not.

sns.countplot(df['target'])
target countplot | heart disease prediction KNN

We will proceed with this as there isn’t much unbalance in target data.

3. Performing KNN by splitting to train and test set:

x= df.iloc[:,0:13].values 
y= df['target'].values
from sklearn.model_selection import train_test_split  
x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25, random_state=0)
from sklearn.preprocessing import StandardScaler    
st_x= StandardScaler()    
x_train= st_x.fit_transform(x_train)    
x_test= st_x.transform(x_test)  

This step is common for all ML tasks and here I have just split the dataset and scaled it for further processing.

4. Checking for the best value of  k:

error = []
# Calculating error for K values between 1 and 30
for i in range(1, 30):
    knn = KNeighborsClassifier(n_neighbors=i)
    knn.fit(x_train, y_train)
    pred_i = knn.predict(x_test)
    error.append(np.mean(pred_i != y_test))
plt.figure(figsize=(12, 6))
plt.plot(range(1, 30), error, color='red', linestyle='dashed', marker='o',
         markerfacecolor='blue', markersize=10)
plt.title('Error Rate K Value')
plt.xlabel('K Value')
plt.ylabel('Mean Error')
print("Minimum error:-",min(error),"at K =",error.index(min(error))+1)
# Output => Minimum error:- 0.13157894736842105 at K = 7

 

finding best value of k

5. Apply K-NN Algorithm:

classifier= KNeighborsClassifier(n_neighbors=7)  
classifier.fit(x_train, y_train)
y_pred= classifier.predict(x_test) 
from sklearn.metrics import confusion_matrix  
cm= confusion_matrix(y_test, y_pred)
# Output =>array([[26,  7],
                 [ 3, 40]], dtype=int64)

This way we can see our confusion matrix. Here I specified the k value as 7 as we got the lowest mean error at 7.

6. Accuracy:

accuracy_score(y_test, y_pred)
# Output => 0.868421052631579

We got 86% accuracy on 25% of the dataset and this is a good sign. We could improve them by performing more hyperparameter tuning.

References:

1. https://www.dataminingbook.com/book/python-edition

2. https://www.kaggle.com/ronitf/heart-disease-uci

3. Image: https://unsplash.com/photos/KgLtFCgfC28

Applications of KNN:

Now we know about KNN and how to implement them. Let’s see some scenarios where KNN is used.

1. Music Recommendation System: Probably any recommendation system. But, in the case of music systems, we have a large amount of music coming and there is a high chance that we are getting the same music with different versions being recommended, These could be analyzed using KNN. We could even use it to see which music is of the person’s liking.

2. Outlier Detection: KNN has the ability to identify outliers.

3. Similar documents can be identified using KNN Algorithm.

Conclusion

You can find the complete code here:

https://github.com/Siddharth1698/Machine-Learning-Codes/tree/main/knn_heart_disease

Feel free to connect with me on:

1. https://www.linkedin.com/in/siddharth-m-426a9614a/

2. https://github.com/Siddharth1698

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
blogathonclassificationKNNmachine learning
Siddharth
Siddharth

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