Building ML Models

Learning Objectives Define the key stages of the machine learning model building process. Explain the critical difference between training data and testing data. Identify features and labels in a given dataset. Describe the purpose of model training and model evaluation. Outline the steps to split a dataset for training and testing. Use the .fit() and .predict() pattern to train a model and make a prediction. Interpret a simple accuracy score to evaluate a model's performance. Ever wonder how Netflix knows exactly what movie you want to watch next? 🎬 You're about to learn the fundamental recipe for teaching a computer to make smart predictions! This tutorial will walk you through the core process of building a machine learning model. We will treat it like a sci...

TermDefinitionExample DatasetA collection of data, usually organized in a table with rows and columns, similar to a spreadsheet or a database table.A spreadsheet of student information, where each row is a different student and columns are 'Hours Studied', 'Previous Grades', and 'Final Exam Score'. FeaturesThe input variables or attributes from the dataset that the model uses to make a prediction. These are the 'clues' the model looks at.In a student dataset, the features could be 'Hours Studied' and 'Previous Grades'. Label (or Target)The output variable that you are trying to predict. This is the 'answer' you want the model to learn.In a student dataset, the label would be the 'Final Exam Score' or a 'P...

The Data Splitting Rule (80/20 Rule) Dataset -> 80% Training Data + 20% Testing Data A common practice is to split your entire dataset into two parts. The larger part (e.g., 80%) is used for training the model, and the smaller part (e.g., 20%) is reserved for testing its performance. You must never train the model on the testing data. The Fit-Predict Pattern 1. model.fit(features_train, labels_train) 2. predictions = model.predict(features_test) This is the core programming pattern for many ML libraries. First, you use the `.fit()` method with your training data to train the model. Second, you use the `.predict()` method with your testing features to get the model's predictions. Accuracy Formula Accuracy = (Number of Correct Predictions / Total Number of Predic...