Machine Learning
Exploring Ghana's Rent Data
This article explores the factors influencing rental prices in Ghana's real estate market using data sourced from a popular rental website. By analyzing features such as the number of bedrooms, amenities, and property condition, the article provides insights for investors to add value to their properties and helps renters make informed decisions. The analysis employs techniques like Principal Component Analysis (PCA) to visualize the relationships between different features and identify dominant factors affecting rental prices.
Spam Filtering, Bayesian Approach
This article explains the implementation of a Naive Bayes spam filter, using Bayes' theorem to classify messages as spam or ham. It covers the algorithm’s theoretical foundation, the dataset and implementation details, and the results, which show over 90% accuracy with minimal computational resources. The article also discusses limitations, such as the assumption of word independence and the challenges of imbalanced data, and suggests potential improvements.
Image Compression with K-Means Clustering
This describes how the unsupervised machine learning algorithm, K-means clustering, can be used to compress images without losing much quality.