# K Nearest Neighbor

2 min readJul 6, 2021

- K in KNN is the number of Nearest Neighbors.
- It is a Supervised learning algorithm.
- It classifies data points based on neighbors i.e. similarity measures. So, the data point is classified by five nearest neighbors.
- This algorithm is based on feature similarity.
- Parament tuning is used to choose k.
- If k is too low the algorithm becomes noisy and if it is too big then the algorithm takes forever to process.

# How to find K?

- Take sqrt(n) number of points whose distance with the given data point is needed, where n is the no of data points.
- Find sqrt(n) number of nearest neighbors of K (where is the number of data points) by finding the distance between them by the following two methods:
- Also, make sure that the number of nearest neighbors i.e. sqrt(n) should odd. if it even then take sqrt(n)-1 number of nearest neighbors.

# 1) Euclidean distance:

# 2) Manhattan Distance:

It is used to find the distance between the real vectors using the sum of their absolute difference.

# When to use KNN?

When the data is:

- labeled
- noise-free
- dataset is small

# Examples-

- Recommendation system(Netflix, Amazon)
- Concept Search
- Handwriting detection
- Image Recognition

# Lazy Learner-

- Memorizes the training data
- No learning phase

# Steps-

- Handle Data(import dataset and split it into training and test set)
- Similarity(Calculate the distance between the two data instances)
- Neighbors(Locate k most similar data instances)
- Response(Generate response from the set of data instances)
- Accuracy(summarizing the accuracy of the predictions)
- Main(Tie all together)

# Hands-on:

To predict whether a person will be diagnosed with diabetes or not refer to my Github link where both the dataset and the prediction is performed:

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