Traditional programming relies on hard-coded rules. Developers write explicit instructions using logic, conditionals, and loops to solve specific problems. For example, to sort a list, the program follows precise steps known as a sorting algorithm.
Procedural programming is a paradigm where code is organized into procedures or functions. Think of it like a cooking recipe: you follow step-by-step instructions to prepare a dish. Every step must be followed in a specific sequence to get the correct result.
Object-Oriented Programming (OOP) focuses on objects—bundles of data and related behavior. Imagine a car: it has properties (like color and model) and functions (like drive or brake). OOP helps build reusable, modular code.
Despite different paradigms, both procedural and object-oriented programming require humans to define logic, rules, and decision flows explicitly. Computers cannot make decisions on their own without being told exactly what to do.
Artificial Intelligence (AI), particularly machine learning, flips that paradigm. Instead of coding rules, developers provide data. The system learns patterns from this data to generate its own 'rules.'
Machine Learning introduces a new approach. Instead of writing rules, we let machines learn from examples. By analyzing large sets of data, machines detect patterns and make predictions or decisions based on those patterns.
CRISP-DM (Cross-Industry Standard Process for Data Mining) was created in the late 1990s to standardize how data mining projects are executed. It includes steps like business understanding, data preparation, modeling, evaluation, and deployment. It remains the most widely used methodology today.
Classification analysis is a supervised learning technique used to categorize data into predefined classes. For example, an email filtering system classifies emails as 'spam' or 'not spam' based on past examples.
Regression analysis predicts a continuous outcome based on input data. A common example is predicting house prices based on features like location, size, and number of rooms.
Clustering analysis is an unsupervised learning method that groups similar data points together. For instance, in customer segmentation, users with similar purchasing behavior are grouped into clusters.
In machine learning, it's crucial to separate training and testing data. If we train and test on the same data, the model might memorize it instead of learning general patterns—leading to overfitting and poor real-world performance.