Python Machine Learning Tutorial: KNN Classification and K-Means Text Clustering
In this video, we will learn how to apply machine learning to Indonesian text analysis using Python. The main topics covered are text classification using the K-Nearest Neighbors (KNN) method and text clustering using the K-Means method.
This video is suitable for students, lecturers, novice researchers, and anyone who wants to understand the basics of text mining, Indonesian language NLP, and machine learning implementation using comment datasets in CSV format.
The video covers:
Reading a CSV dataset containing Indonesian text comments
Preprocessing Indonesian text
Case folding or converting text to lowercase
Removing punctuation, numbers, and special characters
Stopword removal using Sastrawi
Stemming Indonesian text
Text feature extraction using TF-IDF
Text classification using KNN
Classification model evaluation
Text clustering using K-Means
Visualization of clustering results
This video can be used as learning material for courses in Machine Learning, Data Mining, Text Mining, Natural Language Processing, and Python labs.
00:00 - Introduction: Implementation of Classification (KNN) and Clustering (K-Means)
01:12 - Explanation of the Comments Dataset (CSV Format)
03:10 - Preparing the Working Environment using Google Colab
05:20 - How to Upload a Dataset to Google Colab
07:54 - Installing Python Libraries (Pandas, Scikit-learn, Sastrawi)
10:53 - Importing Libraries Required for KNN Classification
16:21 - Reading and Displaying a Dataset using Pandas
19:33 - Preprocessing Stages: Case Folding, Cleaning, Stopwords, and Stemming
30:52 - Definition of Feature Variables (X) and Labels (Y)
31:29 - Data Division: Training Data and Test Data
35:32 - Text Weighting Process using TF-IDF
37:41 - Building a Machine Learning Model with a Pipeline
46:26 - Training the Model KNN (Training Process)
47:51 - Model Evaluation: Calculating Accuracy and Classification Report
53:08 - Prediction Testing with New Comment Data
56:56 - Prediction Results Analysis and Conclusions
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