- Lab 1: Dataset Exploration on 3 datasets (Spotify Top Hit Playlist (2010-2022), Water Quality and Potability and MNIST).
- Lab 2: Data Exploration & Visualization on previous 3 datasets by generating graphs.
- Lab 3: Selected Spotify dataset and applied Feature Reduction and PCA.
- Lab 4: Explored an image dataset called JMuBEN2 (Arabica coffee plantation leaves), applied pre-processing and normalization, trained a CNN model with 50 epochs and an SGD Classifier, and performed Evaluation Metrics on both models.
- Lab 5: Instead of using CNN and SGD Classifier on the previous dataset, different types of Naïve Bayes Classifiers were trained on the dataset, and Evaluation Metrics were recorded.
- Lab 7: Applied different clustering algorithms to detect the optimal number of clusters.
- Lab 11: Exploring effects of different CNN architectures on the JMuBEN2 dataset.
All parts involve exploring different techniques on a subsect of the German Street Sign Recognition Dataset.
- Part 1: Data Visualization and Bayes Net
- Part 2: Clustering
- Part 3: Decision Trees
- Part 4: MLP and CNN