To understand how **Gaussian mixture models (GMMs)** perform on real-world data, we apply them to the well-known **Iris dataset**, which contains measurements of flower species. The algorithm is as follows: 
  

1.  **Exploratory data analysis (EDA)**:before applying GMM, we performed some basic **EDA** on the Iris dataset to understand its structure; 

         

2.  **Training the GMM**:after EDA, the GMM was implemented to cluster the dataset into groups. Since the Iris dataset has three species, we predefined the number of clusters to **3**. During training, the model identified clusters based on the likelihood of each data point belonging to a Gaussian distribution; 

     

3.  **Results**:the model effectively grouped the data into clusters. Some points were assigned to overlapping regions with probabilistic weights, demonstrating GMM's strength in handling real-world data with subtle boundaries; 

     

4.  **Comparing clusters with true labels**:to evaluate the model's performance, the GMM clusters were compared with the actual species labels in the dataset. Although GMM doesn't use labels during training, the clusters closely matched the true species groups, showing its effectiveness for unsupervised learning. 

This implementation highlights how GMMs can model complex real-world datasets, making them versatile tools for clustering tasks. 

Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.

Dive into the fundamentals of clustering and discover how it differs from classification. Explore essential algorithms, tools, and libraries that power this unsupervised learning technique to uncover hidden patterns in data.

Gain a solid understanding of key preprocessing techniques that ensure effective clustering. Learn how to handle missing values, encode categorical features, normalize data, and choose appropriate distance measures and linkages to boost clustering accuracy.

Master the skills needed to apply K-Means clustering effectively. Learn how the algorithm works, determine the optimal number of clusters, and gain hands-on experience by implementing K-Means on both synthetic and real-world datasets.

Explore the essentials of hierarchical clustering and learn how to group data into meaningful clusters using dendrograms. Build confidence in identifying the optimal number of clusters and implementing the technique on both synthetic and real-world datasets.

Discover how DBSCAN excels at detecting clusters of varying shapes and handling noise in data. Learn the mechanics behind this density-based algorithm, how to assign points to clusters, and apply it to both synthetic and real datasets with confidence.

Gain a solid understanding of Gaussian Mixture Models and how they use probability to model complex cluster shapes. Learn the principles of Gaussian distribution, explore how GMMs work, and build confidence by applying them to both dummy and real-world data.

Cluster Analysis

Implementing GMM on Real Data