This a solution notebook to an assignment question given in a Data Mining graduate course. Each code block is accompanied by relevant analysis wherever required.
Dataset link: https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset
Broadly, the following steps have been performed in this solution notebook:

• Plotted the class distribution of the dataset and its analysis.
• Performed EDA (histograms, box plots,etc.) and provided various insights on the data.
• Used TSNE alogorithm to reduce data dimensions to 2 and plotted the resulting data as scatterplot.
• This helps in observing the separability of the data.
• Ran the sklearn implementation of Gaussian Naive Bayes and Multinomial Naive Bayes.
• Reported Accuracy, Recall, and Precision and analyzed the differences in the two implementations of Naive Bayes using the [80:20] train test split
• Used Principal Component Analysis (PCA) to reduce the number of features and used the reduced dataset for model training.
• Retained dfifferent amounts of variance values, ranging from 0.9 to 1 in steps of 0.01.
• Compared the results using Accuracy, Precision, Recall and F1-score.
• Plotted ROC-AUC curves
• Further trained the model using Multinomial Logistic Regression and compared the results with Naive Bayes.