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https://github.com/mouralisandra/machinelearninglabs
A collection of Machine Learning and Data Mining labs for the ML course taught at INSAT.
https://github.com/mouralisandra/machinelearninglabs
agnes cah dendrograms diana kmeans-clustering knn naive-bayes-classifier pca random-forest supervised-learning unsupervised-learning
Last synced: 19 days ago
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A collection of Machine Learning and Data Mining labs for the ML course taught at INSAT.
- Host: GitHub
- URL: https://github.com/mouralisandra/machinelearninglabs
- Owner: mouralisandra
- Created: 2023-12-12T11:06:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-12T12:10:21.000Z (about 1 year ago)
- Last Synced: 2024-10-27T21:36:13.025Z (2 months ago)
- Topics: agnes, cah, dendrograms, diana, kmeans-clustering, knn, naive-bayes-classifier, pca, random-forest, supervised-learning, unsupervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 519 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MachineLearningLabs
A collection of Machine Learning and Data Mining labs for the ML course taught at INSAT.
## Supervised Learning :
During those labs we implemented the :
* `Naive Bayes`
* `KNN`
* `Random Forests`
models on the Iris Dataset and used the test validation with different validation metrics using `train/test split` and `Cross Validation`
## Unsupervised Learning:
* Principal Component Analysis `PCA`
* `KMeans Clustering`
* `CAH` Agglomerative clustering and `Dendrograms`
* `Silhouette` and `Elbow` to determine the best number of clusters
* `Crosstab` to validate between models
* We manually implemented `DIANA` algorithm based on Kmeans
## Screenshots:
### Supervised Learning:
* Classifiers Comparison :
### Unsupervised Learning :
* Coreelation and dispersion analysis :
* Elbow :
* 
* Silhouette :
* Principal Component Analysis :
* Dendrogram of CAH Agglomerative clustering :
* Manual DIANA Implementation :
