Tag: cluster
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Malware Detection & Interpretation – PCA, T-SNE & ML
This post discusses the application of PCA, T-SNE, and supervised ML algorithms for malware detection using a benchmark dataset. Techniques such as Logistic Regression, SVC, KNN, and XGBoost are implemented, achieving high performance metrics. Results show potential for improving malware detection using ML while reducing false positives and enhancing cyber defense.
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Weather Forecasting & Flood De-Risking using Machine Learning, Markov Chain & Geospatial Plotly EDA
Foto door Pok Rie The most frequent natural disaster in the world, flooding affects hundreds of millions of people and kills between 6,000 and 18,000 people annually. Moreover, climatic change has many consequences as surge in frequency of rainfalls potentially enhance the rate of flooding. In this environmental science project, we will explore real-time weather…
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Anomaly Detection using the Isolation Forest Algorithm
The post describes the application of Isolation Forest, an unsupervised anomaly detection algorithm, to identify abnormal patterns in financial and taxi ride data. The challenge is to accurately distinguish normal and abnormal data points for fraud detection, fault diagnosis, and outlier identification. Using real-world datasets of financial transactions and NYC taxi rides, the algorithm successfully…
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Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks
This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…
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Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%
In 2023, the global card industry is projected to suffer $36.13 billion in fraud losses. This has necessitated a priority focus on enhancing credit card fraud detection by banks and financial organizations. AI-based techniques are making fraud detection easier and more accurate, with models able to recognize unusual transactions and fraud. The post discusses a…
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Unsupervised ML, K-Means Clustering & Customer Segmentation
The goal of this post is to describe the most popular, easy-to-use, and effective data-driven techniques that help organizations streamline their operations, build effective relationships with with individual people (customers, service users, colleagues, or suppliers), increase sales, improve customer service, and increase profitability. Customer Segmentation (CS) is a core concept in this technology. This is the…
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Effective 2D Image Compression with K-means Clustering
The post explores the application of the K-means clustering algorithm, a popular unsupervised Machine Learning algorithm, for image compression. By segmenting 2D images into different clusters, the algorithm effectively reduces storage space without compromising on image quality or resolution. It also demonstrates the application of this approach through a case study, where optimal results were…
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Dabl Auto EDA-ML
Dabl, short for Data Analysis Baseline Library, is a high-level data exploration library in Python that automates repetitive data wrangling tasks in the early stages of supervised machine learning model development. Developed by Andreas Mueller and the scikit-learn community, it facilitates data preprocessing, advanced integrated visualization, exploratory data analysis (EDA), and ML model development, demonstrated…
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K-means Cluster Cohort E-Commerce
K-means Clusters – Cohort Analysis applied to E-Commerce Understanding who your customers are and what they want is a fundamental part of any successful business. It can become increasingly challenging to create a one-size-fits-all customer profile. This is where the concept of cluster-based cohort analysis comes in.
