Build a Machine Learning Model with KNN in Python | Iris Dataset Step-by-Step for BeginnersKNN model скачать в хорошем качестве

Build a Machine Learning Model with KNN in Python | Iris Dataset Step-by-Step for BeginnersKNN model

Build a Machine Learning Model with KNN in Python | Iris Dataset Step-by-Step for Beginners In this beginner-friendly tutorial, you’ll learn how to build a Machine Learning classification model using the K-Nearest Neighbors (KNN) algorithm in Python. We use the famous Iris dataset and walk step-by-step through data loading, preprocessing, train-test split, model training, prediction, and evaluation. This lecture also explains every library used in the code — including pandas, numpy, matplotlib, seaborn, and scikit-learn — so new learners can clearly understand what each tool does. By the end of this video, you will be able to create, train, and evaluate your own ML classifier and visualize results using a confusion matrix heatmap. What you’ll learn: What KNN algorithm is and how it works How to use sklearn with Python How to split training and testing data How to measure model accuracy How to read classification reports How to plot a confusion matrix Explanation of all libraries used Perfect for: Beginners in Machine Learning, Python students, CS learners, and developers starting AI/ML. knn python, knn machine learning, iris dataset tutorial, machine learning for beginners, python ml tutorial, sklearn knn example, k nearest neighbors python, classification in machine learning, iris dataset classification, scikit learn tutorial, train test split python, confusion matrix python, classification report sklearn, python data science tutorial, beginner ml project, supervised learning python, knn explained, machine learning step by step, python ml project, pandas sklearn tutorial