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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.
Skills
Data Analysis
Data Plotting
Machine Learning
Neural Networks
Data Visualization
Prerequisites
Basics of Python
Basics of MiniSom
Basics of machine learning
Technologies
Pandas
Python
seaborn
Matplotlib
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
1
Introduction
Task 0: Get Started
Task 1: Import Libraries
2
Data Preparation
Task 2: Load and Visualize the Dataset
Task 3: Preprocess the Dataset
3
Training
Task 4: Train the SOM
Task 5: Get the Coordinates of the Winning Neurons
4
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
Congratulations!