Ever felt lost in Python's universe of data types? ๐
Then dive into the basics with me today!
Today we're exploring Booleans, Integers, and Floats โ the core elements in Python's data type galaxy! ๐๐
These data types are like the atoms of Python.
We start with them individually and later combine them into larger structures like lists and dictionaries.
It's a journey from simplicity to complexity!
1๏ธโฃ ๐๐ข๐ข๐๐๐๐ก๐ฆ
Booleans are the on/off switches of Python ๐ฆ.
Booleans decide your code's route, guiding it with simple yet powerful TRUE or FALSE signals.
They're the silent guardians of logic!
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Integers are the digital legos in Python ๐งฑ
From 0 to infinity...
they're the countable stones that pave the path of loops and arrays in your code.
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Floats are the Artists of Precision ๐จ
They're the precision points that give your calculations depth and detail, just like the delicate brush strokes on a canvas.
๐ผ ๐๐ฎ๐ฟ๐บ๐ผ๐ป๐ถ๐๐ถ๐ป๐ด ๐ก๐๐บ๐ฏ๐ฒ๐ฟ๐ - ๐ง๐ต๐ฒ ๐๐ป๐๐ฒ๐ด๐ฒ๐ฟ-๐๐น๐ผ๐ฎ๐ ๐๐ฎ๐น๐น๐ฒ๐
Mixing Integers and Floats in Python is like a duet in music ๐ถ
Python conducts the harmony, elevating Integers to Floats for a symphony of precise computations.
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Transforming data types in Python is like a wizard's spell ๐งโโ๏ธ.
Changing data types in Python is like casting a spell. With a flick of a function, watch a Boolean transform into an Integer or a Float!
Understanding Python's data types is like mastering the alphabet of a new language. ๐
It empowers you to write code that's clear, efficient, and impactful.
Ready to experiment with these fundamental types?
And this is all for now! ๐ค
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We are going back to the basics to simplify ML algorithms.
... today's turn is Multiple Linear Regression! ๐๐ป
In MLR, imagine you're baking.
You've got different ingredients or variables.
You need the perfect recipe (model) for your cake (prediction).
Each ingredient's quantity (coefficient) affects the taste (outcome).
1๏ธโฃ ๐๐๐ง๐ ๐๐๐ง๐๐๐ฅ๐๐ก๐ ๐ฃ๐๐๐ฆ๐
We're using height and weight - a classic duo often assumed to have a linear relationship.
But assumptions in data science? No way! ๐ง
Let's find out:
- Do height and weight really share a linear bond?
Today let's exemplify SQL's execution order with a simple query๐๐ป
1๏ธโฃ ๐ฆ๐ง๐๐ฅ๐ง๐๐ก๐ ๐๐ฅ๐ข๐ ๐ข๐จ๐ฅ ๐ฅ๐๐ช ๐ง๐๐๐๐
We use a dummy table with the salary of employees depending on their field and experience,
๐ฏ Our main goal?
Understand the field that earns the most.
2๏ธโฃ ๐ฆ๐ค๐ ๐ค๐จ๐๐ฅ๐ฌ ๐ฆ๐ง๐ฅ๐จ๐๐ง๐จ๐ฅ๐ (to use)
We define a query to obtain our goal data.
Today I am starting with a new ML model
... so it is the turn of the Support Vector Machine! ๐๐ป
0๏ธโฃ ๐ฅ๐๐๐๐ฃ
SVM is a ML method that finds the optimal hyperplane separating classes by maximizing margin, using support vectors to ensure the greatest distance between class data points.
1๏ธโฃ ๐ ๐๐ง๐๐๐ ๐๐ง๐๐๐๐ ๐๐ก๐ง๐จ๐๐ง๐๐ข๐ก ๐งฎ
To classify our data, we apply some intuition:
The dot product is the projection of one vector along with another. So we can use it to determine whether a data point is one class or the other.