Real Life Use Case & Concepts of Linear Transformation in Data Science, Machine Learning?

 

Feature Scaling (Min-Max Scaling, Standardization)

Linear Transformation is used to scale numerical data so that all features fall within the same range.

Example:

 Bringing sales figures from ₹1,000 – ₹1,00,000 into a range of 0 to 1 using Min-Max Scaling Formula to help maching learning models treat all dimensions equally.

This Ensures Machine Learning Models can Handle All Dimensions Equally.

Bringing student marks from 0 – 100 into a range of 0 to 1 using the Min-Max Scaling formula to help machine learning models treat all Subjects equally.





Normalization


It is used to change values that are on different scales into the same scale without changing the meaning or difference between them.

Making Different Data types understandable and in Common Format. However this does not affect the Values, but only the data type changes to Common Format. So Computer can process the Information Equally.

Example:

Normalizing Age, Income, and Expenses for fair comparison using Min-Max Scaling formula or Standardization Formula.





Linear Regression Models


This Method uses Straight Line to do the Calculation or Prediction. Using Historical and past data, it does repeated Calculations and predictions to give future values.

Linear Regression applies a linear formula:

Y = mX + c




This is a direct example of linear transformation in predictive modeling.

It is one of the simplest models in machine learning, easy to understand, interpret, and apply.


Principal Component Analysis (PCA)


PCA uses linear transformations to rotate and scale data to reduce dimensions while retaining Important patterns of Data.

PCA is used to reduce the number of variables (dimensions) in a dataset while still keeping the most important patterns.

Simple Example:

You have a dataset with:

  • Height
  • Weight
  • Age
  • Income


PCA applies linear transformations to rotate and scale these values into new dimensions like:


Principal Component 1 → captures combined patterns of Height + Weight

Principal Component 2 → captures combined patterns of Age + Income


Another Example:-





PCA reduces many features into just 2 or 3 important combined features by rotating and scaling the data, so we can save time, space, and focus only on the main patterns. Then you can create Graphs to visualize the Data.



Neural Networks (Deep Learning)

Every neuron in a neural network applies a linear transformation (like matrix multiplication) on the data and then applying an activation function.

In a neural network, each neuron first does a simple math calculation (adding and multiplying the inputs) to weight each Input values and their Importance,

then uses an activation function to decide whether this Neuron (data) should be activated or not.

This help the network learn complex patterns and provide realistic and best answer/ best decision.

 Just like a neural network, your brain:

  • Collects Multiple inputs.

  • Weighs them and decides which inputs are important.

  • Makes a decision based on a rule.






Why Linear Transformation is Important in Data Science:

  • It makes different types of data comparable by scaling or shifting values.

  • It improves the accuracy and stability of machine learning models.

  • It helps in dimensionality reduction and efficient computation.

Comments

Post a Comment

Popular posts from this blog

What is Artificial Intelligence? What is Machine Learning? What is Data Science? how they are related to each other?

Image
Artificial Intelligence means making Computers think like Human Brain, choosing correct & realistic decisions, answers based on the Current circumstance and importance. Personal Example: Data Science = What is Actually Happening in your Life. Machine Learning = What you are learning and experiencing in your Life. Artificial Intelligence = Taking Correct Decisions based on your Life Experience Another Example: You are AI Engineer & you created a Personal AI Friend using Python. Now, You give all information about your Financial Status, Economic Status, Emotional Status, Social Status, Current Situation in your Life, Current Situation in your Family, Current Situation in your Country etc etc... and your Current problems in an Excel file or SQL Database. Now you are giving this data to your AI tool which you created. 1. It will analyze the information, choose which is important and which is useless based on your current situation. 2. Now, it sends that important Information to Mac...
Read more

Linear Algebra - What is it?

Linear Algebra is a branch of mathematics that deals with vectors , matrices , and linear transformations . It's one of the foundations of data science, machine learning, and computer graphics. In Simple Words : Linear algebra helps us: Solve equations with multiple variables ( variables - a value, number or anything that changes anytime eg, Mark, Temperature, Price ) Represent and transform data using matrices ( Matrix - numbers arranged in rows and columns) Understand how systems behave (like how multiple inputs affect an output) Key Concepts : Scalars – Scalar means Magnitude(Speed). So it is just a single number (e.g., 8 or 6 or 10 etc..) Vectors – Vector means Both Magnitude (Speed) and Direction. So it is a ordered list of numbers (e.g., [2, 3] or [4, 5] or [10, 50] etc..) Matrices – A grid of numbers arranged in rows and Columns  (e.g., a 2x2 matrix or 4x4 matrix) Matrix Multiplication – Combining 2 or more matrices to transform data. Linear Transformation - Multiply...
Read more

What is a Python Library?

When we use Python for data analysis, machine learning, or any programming, we do not write each and every code. Why Should we? Some experts already wrote code for us and packed it as a Library. This reusable collection of code is called a Python Library . A library in Python is like a ready-made toolbox. Instead of building every tool yourself, you take the tools from the toolbox and start using it in your code. Why do we need Python Libraries? Save time – We do not write the same code again and again. Easy to use – Just install, import, and start using. Powerful features – Some libraries can do complex tasks in one line of code. Community support – Big communities maintain them, so they keep improving. Examples of Python Data Science Libraries: Pandas – Works with data tables like Excel, filtering and summarizing data NumPy – Works with numbers, arrays, mathematical calculations Matplotlib – Creates graphs and charts Scikit-learn – For machine learning models like regres...
Read more