Category: Data 219

After conduction KNN-classification with Culmen Depth and Flipper Length to predict island with the penguin dataset and K being odd numbers from 1 to 40, I got the following F1 scores: F1 for k = 1 is: 0.6369527889419905F1 for k = 3 is: 0.6564557334952033F1 for k = 5 is: 0.6394099629071051F1 for k = 7 is: 0.6740437952239902F1…

Feature Accuracy Precision Recall F1 Gender 0.6532 0.6480 0.6532 0.6500 Daytime/evening attendance 0.6134 0.5074 0.6134 0.5200 Scholarship holder 0.6122 0.3749 0.6122 0.4650 Tuition fees up to date 0.7221 0.7886 0.7221 0.6758 International 0.6070 0.3684 0.6070 0.4585 Debtor 0.6838 0.7070 0.6838 0.6243 Educational special needs 0.6088 0.3709 0.6088 0.4609 One feature contingency table for “tuition fees…

Part 1: Part 2: https://www.kaggle.com/datasets/piterfm/olympic-games-medals-19862018 I chose this dataset because I swim and swimming is an Olympic sport, as well as the wide variety of countries that participate allow for many possible probabilities to be computed. Given these results I would like to next try to predict what medal a country is most likely to…

The dataset I selected was a listing of the top 200 all-time swims in a variety of Olympic events. This is the link to access the dataset https://www.kaggle.com/datasets/thedevastator/swimming-top-200-world-times-in-each-category After downloading the dataset and performing some initial data exploration, I determined the following: After this initial exploration, I started more analysis about the contents the dataset…