It is the simplest form of Regression Analysis
There is a group of people that I measured, and the 2 features that I measured of these people are their weight and their high level.
Making it clear the weight on the x-axis and the high level on the y-axis.
Plotting the points of people’s Weight against their heights. And I can see that there is a Linear relationship.
Did you ever wonder how the spam classifier in your email works? How does it know that the email might be spam or not?
One popular technique is something called, Naive Bayes, and that’s an example of a Bayesian method, so let’s learn more about how that works.
So what is machine learning? Well if you look it up on the internet it’ll say that it’s algorithms that can learn from observational data and can make predictions based on it.
But in reality, these techniques are usually very simple: we take a set of observational data, we fit a line to it, and then we can use that line to make predictions.
Recently I was training a machine learning model and found something which I have never experienced before. The model I trained worked well in the validation and testing phase.
As I deployed it and tested it against real-life cases, its accuracy just dropped drastically. I searched, and that's when I learned about Data Leakages.
Data Leakage is the use of information in the model training process which would not be available at prediction time, causing the predictive scores (metrics) to overestimate the model’s utility when running in a production environment.
Let me explain the complex definition, we usually split datasets…
Have you ever been in a situation where you don't know whether to use Analysis or Analytics?
Which word to use?
The similarity of the words: Analysis and Analytics lead people to believe they share the same meaning and use them interchangeably.
Technically, this isn’t correct. There is in fact a distinction between the two.
So let’s clear this up.
First, we will start with Analysis.
Consider you have a vast data set containing data of various types.
We will build a simple form of Object Recognition System. Although the example we’ll use is very simple, it reflects many of the same key machine learning concepts that go into building real-world commercial systems.
The dataset we will use is a small, very simple, for training a classifier to distinguish between distinct types of fruit.
To create the original dataset, we go to a nearby store, bought a few dozen oranges, lemons, and apples of different varieties, and recorded their measurements in a table. We notice the height and the width, estimated their mass.
Data can be visualized by representing it as plots easy to understand, explore, and grasp. To analyze a set of data using Python, we make use of Matplotlib, a widely implemented 2D plotting library. Seaborn is basically a visualization library that sits on top of Matplotlib and all it does is make it a little prettier to look at.
Seaborn is an open-source that provides high-level API for visualizing the data using Python programming language. It also has a bunch of unique kinds of charts and graphs that we didn’t have in Matplotlib.
Import Aspects of Seaborn
The basic technical idea of Data Science has been around for decades but why is Data Science with Machine Learning algorithms working so well in recent years? And why is that country like Pakistan unable to take advantage of such a growing market?
Over the last 10 years, we went from having a relatively small amount of data to having often a fairly enormous amount of data and all of this was possible because of digitization of a society where so much human activity is now in the digital form. We spend so much time on computers, websites, mobile apps…
Conceived in the late 1980s, Python Language grew to be one of the dominating developing tool in the industry. Because of its elegance and simplicity, Python is used in areas like Web Development, Data Science, Machine Learning Frameworks, and even Video Game Development.
Even with its versatility and plain syntax, learners keep away from some basic yet powerful functions Python offers. In this article, we will dive into those underrated function, beginners shy away from.
Before we start carefully see the terminologies to understand the working of map() function.
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