Anything that is unusual and deviates from the standard “normal” is called an Anomaly or an Outlier. In this post, I will be using Multivariate Normal Distribution
_All the code files will be available at : [https://github.com/ashwinhprasad/Outliers-Detection/blob/master/Outliers.ipynb_](https://github.com/ashwinhprasad/Outliers-Detection/blob/master/Outliers.ipynb)
Anything that is unusual and deviates from the standard “normal” is called an Anomaly **or an Outlier.**
Detecting these anomalies in the given data is called as anomaly detection.
For more theoretical information about outlier or anomaly detection, Check out :** How Anomaly Detection Works ?**
*Case 1 : *Consider a situation where a big manufacturing company is manufacturing an airplane. An airplane has different parts and we don’t want any parts to behave in an unusual way. these unusual behaviours might be because of various reasons. we want to detect these parts before it is fixed in an airplane else the lives of the passengers might be in danger.
*Case 2: *As you can see in the Above Image, how outliers can affect the equation of the line of best fit. So, before performing it is important to remove outliers in order to get the most accurate predictions.
In this post, I will be using Multivariate Normal Distribution
In this article, I will take you through am Emotion Detection Model with Machine Learning. Detection of emotions means recognizing the
How To Plot A Decision Boundary For Machine Learning Algorithms in Python, you will discover how to plot a decision surface for a classification machine learning algorithm.
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
What is neuron analysis of a machine? Learn machine learning by designing Robotics algorithm. Click here for best machine learning course models with AI
Python For Machine Learning | Machine Learning With Python, you will be working on an end-to-end case study to understand different stages in the Machine Learning (ML) life cycle. This will deal with 'data manipulation' with pandas and 'data visualization' with seaborn. After this an ML model will be built on the dataset to get predictions. You will learn about the basics of scikit-learn library to implement the machine learning algorithm.