Hands on KNN using Scikit-learn
Hands-on KNN Classification Using Scikit-Learn in Python
In this comprehensive tutorial, learn how to perform K-nearest neighbors (KNN) classification using Python and Scikit-Learn. We start by setting up the environment in Visual Studio Code and installing necessary libraries, including Seaborn, Pandas, and Scikit-Learn. The video then dives into exploratory data analysis (EDA) using the Wine dataset, examining feature names, target names, and performing data frame manipulations. We generate pair plots and a correlation heat map to identify and drop redundant or low-correlation features. After cleaning the data, we build and train a KNN model, tuning hyperparameters with an elbow plot, and discuss best practices to avoid data leakage. Finally, we evaluate the model using confusion matrices and classification reports. Don't forget to stay until the end to grasp the full process from data preparation to model evaluation.
00:00 Introduction to KNN Classification with Scikit-Learn
00:15 Setting Up the Environment
01:15 Loading and Exploring the Wine Dataset
03:28 Data Preparation and EDA
07:52 Feature Selection with Correlation Heatmap
10:43 Implementing K-Nearest Neighbors (KNN)
14:40 Hyperparameter Tuning with Elbow Plot
21:16 Conclusion and Best Practices