https://github.com/annaanastasy/clustering-fish-species
A comprehensive project demonstrating the use of various clustering techniques to analyze and group fish data effectively.
https://github.com/annaanastasy/clustering-fish-species
clustering-algorithm data-science data-visualization machine-learning-algorithms unsupervised-clustering unsupervised-machine-learning
Last synced: 3 months ago
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A comprehensive project demonstrating the use of various clustering techniques to analyze and group fish data effectively.
- Host: GitHub
- URL: https://github.com/annaanastasy/clustering-fish-species
- Owner: AnnaAnastasy
- License: mit
- Created: 2024-12-11T14:14:46.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-11T14:28:50.000Z (6 months ago)
- Last Synced: 2025-02-12T00:44:46.404Z (4 months ago)
- Topics: clustering-algorithm, data-science, data-visualization, machine-learning-algorithms, unsupervised-clustering, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/annastasy/fish-clustering-diverse-techniques
- Size: 624 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Fish Clustering Using Diverse Techniques
This project demonstrates various clustering techniques applied to a dataset related to fish. The primary goal is to explore, visualize, and cluster data effectively using different methods, providing insights into patterns and groupings within the dataset.
---
## Introduction
Clustering is an unsupervised machine learning technique used to group data points based on similarity. This notebook showcases various clustering approaches applied to a fish dataset, including:
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Spectral Clustering
These techniques are evaluated and visualized to compare their effectiveness.
## Technologies Used
The notebook leverages the following tools and libraries:
- Python (3.x)
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
## Notebook Structure
The notebook is organized into the following sections:
1. **Introduction and Objective**: An overview of the clustering problem and dataset.
2. **Data Loading and Preprocessing**: Reading and cleaning the fish dataset.
3. **Exploratory Data Analysis (EDA)**: Visualizations and statistical summaries to understand the data.
4. **Clustering Techniques**:
- Implementation of K-Means, Hierarchical, DBSCAN, and Spectral clustering.
- Visualization of clusters.
5. **Comparison and Evaluation**: Metrics and observations comparing the methods.
6. **Conclusion**: Summary of findings and insights.
## Installation and Setup
1. Clone the repository:
```bash
git clone https://github.com/AnnaAnastasy/Clustering-Fish-Species
```
2. Download the dataset from provided link: [Fish species sampling data](https://www.kaggle.com/datasets/taweilo/fish-species-sampling-weight-and-height-data) and save it in the same directory as the notebook.
3. Install the required libraries:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn
```
4. Launch the Jupyter Notebook:
```bash
jupyter notebook
```
5. Open `fish-clustering-diverse-techniques.ipynb` in your Jupyter environment.
## License
This project is licensed under the MIT License. See the LICENSE file for details.
---
**Author**: Anna Balatska
Feel free to reach out for any questions or suggestions!