Bu dosyada OLC731 dersinin 11. haftasına ilişkin ders notları yer almaktadır.

PPV isolates from wild boars in Russia.

This is a project looking at the attributes of the top 5 male and female fighters in each weight class in the UFC.

In this project, Principal Component Analysis (PCA) is applied to a housing dataset to explore the relationships between various housing features and prices. The analysis begins with a detailed data cleaning and exploratory data analysis (EDA) phase, followed by the application of PCA to reduce the dataset's dimensionality. Using statistical tests like Bartlett's test and the Kaiser-Meyer-Olkin (KMO) test, we evaluate the dataset's suitability for PCA. The results highlight the contributions of key variables and principal components, offering actionable insights while simplifying the dataset for further analysis. This project serves as a practical example of using PCA to handle high-dimensional data in the real estate sector, helping to streamline analysis and improve interpretability without sacrificing essential information.

This analysis explores the clustering behavior of clubgoers based on various preferences, entry times, and demographic information. Using unsupervised learning techniques, we compare the performance of K-Means and DBSCAN clustering algorithms. The optimal number of clusters is determined using the elbow method, and the clustering results are visualized using PCA (Principal Component Analysis) to reveal distinct patterns within the data. DBSCAN's ability to detect irregular clusters and noise is compared with K-Means' performance on well-separated clusters. This study highlights the strengths and limitations of both algorithms in identifying meaningful segments among clubgoers.