MACHINE LEARNING RECIPES
DATA CLEANING PYTHON
DATA MUNGING
PANDAS CHEATSHEET
ALL TAGS
# How to compute the euclidean distance between two arrays?

# How to compute the euclidean distance between two arrays?

This recipe helps you compute the euclidean distance between two arrays

How to compute the euclidean distance between two arrays?

Euclidean distance is the distance between two points for e.g point A and point B in the euclidean space. This distance can be find in the numpy by using the function "linalg.norm". Lets understand this with practical implementation.

```
import numpy as np
```

```
data_pointA = np.array([5,6,7])
data_pointB = np.array([8,9,10])
```

```
Euclidean_distance = np.linalg.norm(data_pointA - data_pointB)
print("The Euclidean distance between two points are:", Euclidean_distance)
```

The Euclidean distance between two points are: 5.196152422706632

Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.