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Chemicals Distillation Using Self-organizing Maps

In this project, we’ll use self-organizing maps (SOMs) and heat maps to visualize data patterns in ceramics according to their chemical composition.

Chemicals Distillation Using Self-organizing Maps

You will learn to:

Implement the self-organizing map (SOM) algorithm.

Visualize data patterns using heatmaps.

Analyze neuron influences within the SOM grid.

Preprocess a complex dataset.


Data Analysis

Data Plotting

Machine Learning

Neural Networks

Data Visualization


Basics of Python

Basics of MiniSom

Basics of machine learning






Project Description

This project utilizes self-organizing maps (SOMs), an artificial neural network renowned for its effectiveness in dimensionality reduction and data visualization. An SOM facilitates the comprehension of intricate data patterns by organizing high-dimensional data into a 2D grid while preserving relationships. Moreover, SOMs are particularly valuable for clustering and exploratory data analysis because they employ a competitive learning process.

The primary objective of this project revolves around the analysis of ceramic samples’ chemical composition through the utilization of SOMs. By employing SOMs, we will effectively group similar ceramics based on their chemical composition to identify meaningful patterns and relationships amongst the diverse ceramic samples.

In this project, we will use the MiniSom library to implement an SOM. The dataset is loaded and preprocessed, including data normalization. We will visualize the trained SOM using heatmaps and provide insights into neuron relationships and ceramic clustering. The final plot will display the ceramics’ names at their respective positions on the SOM, revealing composition-based clusters. Additionally, the influence of neurons will be analyzed and plotted to understand their importance in the clustering process.

Project Tasks



Task 0: Get Started

Task 1: Import Libraries


Data Preparation

Task 2: Load and Visualize the Dataset

Task 3: Preprocess the Dataset



Task 4: Train the SOM

Task 5: Get the Coordinates of the Winning Neurons


Plotting Results

Task 6: Get the Coordinates of Each Ceramic in the Heatmap

Task 7: Plot the Heatmap

Task 8: Get the Sum of Weights