In my first exam for the Data Mining course at ITU in Copenhagen, I tackled the challenge of building KMeans and Naive Bayes algorithms from scratch, as well as pre-processing data and performing exploratory data analysis (EDA).
The primary objective of the assignment was to gain expertise in various data mining techniques. To accomplish this, I focused on:
- Cleaning and preparing data for analysis
- Understanding the data and formulating relevant questions
- Developing a clustering algorithm (K-means), executing it, and interpreting the outcomes
- Developing an unsupervised prediction algorithm (Naive Bayes), executing it, and interpreting the results
Overall, this experience helped me gain a deeper understanding of the intricacies of data mining and enabled me to hone my skills in data pre-processing, EDA, and algorithm development.