Country Anthems Clustering & Analysis
This project performs unsupervised learning analysis on country anthems using text clustering techniques. The project analyzes 190 country anthems to discover patterns, similarities, and groupings based on textual content. It employs advanced NLP preprocessing (tokenization, stemming, lemmatization, stop word removal), TF-IDF vectorization, and multiple clustering algorithms (KMeans, Agglomerative Clustering) to identify thematic or linguistic similarities among national anthems. The project includes comprehensive text cleaning, Unicode normalization, silhouette score evaluation, and dendrogram visualization for cluster analysis.
Overview
This project performs unsupervised learning analysis on country anthems using text clustering techniques. The project analyzes 190 country anthems to discover patterns, similarities, and groupings based on textual content. It employs advanced NLP preprocessing (tokenization, stemming, lemmatization, stop word removal), TF-IDF vectorization, and multiple clustering algorithms (KMeans, Agglomerative Clustering) to identify thematic or linguistic similarities among national anthems. The project includes comprehensive text cleaning, Unicode normalization, silhouette score evaluation, and dendrogram visualization for cluster analysis.
Key Features
Text clustering of 190 country anthems
Comprehensive NLP preprocessing (tokenization, stemming, lemmatization)
TF-IDF vectorization for text feature extraction
Multiple clustering algorithms (KMeans, Agglomerative Clustering)
Silhouette score evaluation for cluster quality
Hierarchical clustering with dendrogram visualization
Unicode normalization for multilingual text
Custom word removal (country names, nationalities) to prevent bias
Missing value handling and data cleaning
Cluster visualization and analysis
pages.portfolio.projects.country_anthems_clustering.features.10
Technical Highlights
Analyzed 190 country anthems using unsupervised learning
Implemented multiple clustering algorithms (KMeans, Agglomerative)
Comprehensive NLP preprocessing with NLTK
TF-IDF vectorization for high-dimensional text features
Silhouette score analysis for optimal cluster selection
Handled multilingual text with Unicode normalization
Challenges and Solutions
Multilingual Text
Used Unicode normalization (unidecode) and accent removal for international text handling
Text Preprocessing Complexity
Implemented multiple preprocessing steps (stemming, lemmatization, stop word removal) to balance meaning preservation and noise reduction
High Dimensionality
Used sparse matrix representation and normalization for efficient handling of high-dimensional TF-IDF features
Optimal Cluster Number
Applied silhouette score analysis and dendrogram inspection to determine best number of clusters
Country Name Bias
Removed country names and nationalities from text to prevent clustering bias
Missing Data
Implemented data imputation and careful handling of missing country codes and empty anthems
Technologies
NLP
Clustering
Feature Extraction
Evaluation
Preprocessing
Data
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Unsupervised Learning Pipeline with Text Preprocessing and Clustering
- Category
- Data Science