Data science is full of technical jargon. Here are some essential terms explained simply:
Algorithm: A set of rules a computer follows to solve a problem or make predictions.
Model: A mathematical representation trained on data to recognize patterns or make decisions.
Feature: An individual measurable property or input used in a model (like age, salary).
Label: The target output the model tries to predict (e.g., "spam" or "not spam").
Training Data: The data used to teach a model.
Overfitting: When a model performs well on training data but poorly on new data.
Accuracy: How often the model gets it right.
Confusion Matrix: A table to evaluate prediction results (TP, FP, FN, TN).
Clustering: Grouping similar data points together without labels.
Understanding these terms helps decode the data science world!