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 :