For beginners, the terminology “* Machine Learning*” seems something very complicated and difficult. There is no doubt that it is one of the most rapidly developing fields but that doesn’t mean it has to be too complex. In this tutorial, we will be looking at a very simple, yet useful algorithm called the “

**”.**

*K-Nearest Neighbor Algorithm*We have all heard the quote:

“you are defined by the company you keep”

KNN takes this literally 😁. This will be clearer when we look at the algorithm.

The entire code used in this tutorial can be found here.

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### Understanding the Algorithm:

KNN is a supervised algorithm i.e., it requires a labeled training dataset to work. Let's create a story for ease of understanding. Below we can see that we have 3 different settlements (3 different data points; red, green, purple).

OMG, a wild blob appears!! (a test data point). It looks lost, What will we do now? 😲

Fear not, for we know the KNN algorithm.

We just calculate the distance (Euclidean distance in mathematics terms) of the wild blob from every house in each settlement.

**!! Remember we need to calculate the distance with every other data point, the illustration shows fewer lines because perhaps the illustrator was ‘****ehm ehm’**** a little lazy **🙃 !!

Now, all we do is select the N closest points to our blob. (* N here is a hyperparameter i.e., a number which we must optimally decide ourselves*)

Now let’s see which settlement exists most amongst the closest N points. The Red settlement has more points in the vicinity of our wild blob so the wild blob now becomes part of the Red settlement (It is given the label; Red).

We do this for every test data point. And that is it, that is the algorithm.

Okay, storytime is over, let’s get to coding.

Let us import the necessary libraries.

The dataset we will use for this demo is called the **Iris** dataset. This is an opensource dataset that can be found on the following link: Iris Flower Dataset | Kaggle

Let us explore this dataset.

We have a total of 4 input features and the name of the flower category as our output labels.

Convert the output text label to numeric representation.

```
#Seperating the input features and output labels
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
#converting text labels to numeric form
labels, unique = pd.factorize(y)
```

Let’s code our simple algorithm.

As always, we need to split our data into test and train samples.

```
#splitting data in test and train segments
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size = 0.40)
```

We have used a 60–40 split for the total data.

With all the steps in place, it’s time to test the accuracy.

We have used a helper function in the above code which is below.

```
#Creating a helper function
def takeSecond(elem):
return elem[1]
```

With all the steps in place, it’s time to test the accuracy.

```
#getting predicted values using our algorithm
predictions = list(map(KNNClassify, X_test))
```

calling predictions...

```
#calling the accuracy function
accuracy(predictions, y_test)
```

96.67%!! That’s a very good number.

### Conclusion

This might just be the smallest code we have to write for a machine learning algorithm but it yields an accuracy of 96.67%. Our initial statement stands, ML algorithms don’t need to be complex, they can be as simple as the KNN we just learned in this article. It would be fun to try this algorithm on multiple datasets and see how it performs on those.

Thank you for reading.