Tag: scikit-learn

  • Titanic Benchmark Hypothesis Testing in Disaster Risk Management: (Auto)EDA, ML, HPO & SHAP

    Titanic Benchmark Hypothesis Testing in Disaster Risk Management: (Auto)EDA, ML, HPO & SHAP

    This project aims to apply the Titanic benchmark to hypothesis testing in disaster risk management. Using the Titanic dataset on Kaggle, a Machine Learning (ML) analysis was performed to determine the statistical significance relation between a person’s death and their passenger class, age, sex, and port of embarkation. The project involved comprehensive ML pipeline implementation…

  • Malware Detection & Interpretation – PCA, T-SNE & ML

    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.

  • H2O AutoML Malware Detection

    H2O AutoML Malware Detection

    This study explores AI-powered malware detection using the H2O AutoML algorithm for effective and rapid classification of PE files. The optimized Stacked Ensemble model achieved high precision, recall, and F1 score. The research validates the H2O AutoML workflow’s accurate malware identification and supports related R&D products and solutions in the field of information security.

  • Leveraging Predictive Uncertainties of Time Series Forecasting Models

    Leveraging Predictive Uncertainties of Time Series Forecasting Models

    Featured Image via Canva. In this post, we will take a deep dive into issues around predictive uncertainties of Time Series Forecasting (TSF) models. Our final goal is to minimize decision-making risks. TSF is a prediction of what will occur in the future, and it is an uncertain process. Because of the uncertainty, the accuracy…

  • Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    We predict the Remaining Useful Life (RUL) of NASA turbofan jet engines by comparing the statsmodels OLS, ML SciKit-Learn regression vs LSTM Keras in Python. The input dataset is the Kaggle version of the public dataset for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.

  • Health Insurance Cross Sell Prediction with ML Model Tuning & Validation

    Health Insurance Cross Sell Prediction with ML Model Tuning & Validation

    The content discusses the use of AI and Machine Learning (ML) for insurance cross-selling. It covers topics such as data preparation, model training with different algorithms, parameter optimization, and model evaluation. The study showcases the ability of ML models (HGBM, XGBoost, Random Forest) to predict cross-sell customers in the insurance sector, providing potential for improved…

  • Weather Forecasting & Flood De-Risking using Machine Learning, Markov Chain & Geospatial Plotly EDA

    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…

  • Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App

    Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App

    The objective of this project is to democratize and advance ML/AI for everyone by building the large open-source showcase of Hugging Face NLP, Streamlit/Dash Jupyter PyGWalker Exploratory Data Analysis (EDA), TensorFlow Keras and Gradio App deployment. There is no better way of showcasing your results than creating an interactive demo to try them out! This…

  • Real-Time Anomaly Detection of NAB Ambient Temperature Readings using the TensorFlow/Keras Autoencoder

    Real-Time Anomaly Detection of NAB Ambient Temperature Readings using the TensorFlow/Keras Autoencoder

    The content covers a detailed guide on implementing anomaly detection in time series data using autoencoders. The tutorial utilizes Python and real-world temperature dataset from Numenta Anomaly Benchmark (NAB). Following the Python workflow, the algorithm imports required libraries, performs anomaly detection, and visualizes anomalies. A trained autoencoder model identifies anomalies, with Precision, Recall, and F1…

  • Supervised ML Room Occupancy IoT

    Supervised ML Room Occupancy IoT

    The article presents a study on applying machine learning (ML) to IoT sensor data for workspace occupancy detection. Comparing 14 popular scikit-learn classifiers, the ML systems built use the gathered IoT sensor data to predict room occupancy with high certainty. The results suggest temperature and light are the significant factors affecting occupancy detection. The study…

  • WA House Price Prediction: EDA-ML-HPO

    WA House Price Prediction: EDA-ML-HPO

    A predictive model of house sale prices in King County, Washington, was developed using multiple supervised machine learning (ML) regression models, including LinearRegression, SGDRegressor, RandomForestRegressor, XGBRegressor, and AdaBoostRegressor. The best-performing model, XGBRegressor, explained 90.6% of the price variance, with a RMSE of $18472.7. These results, valuable to local realtors, indicate houses with a waterfront are…

  • ML Prediction of High/Low Video Game Hits with Data Resampling and Model Tuning

    ML Prediction of High/Low Video Game Hits with Data Resampling and Model Tuning

    The post outlines a ML-based approach to forecast video game sales, using several techniques to enhance training, accuracy, and prediction. The Kaggle’s VGChartz dataset, containing sales data and other game-specific information, was used to build and refine the model. Several ML techniques including RandomForestClassifier and Logistic Regression yielded top predictors, with the critic’s score deemed…

  • Comparison of 20 ML + NLP Algorithms for SMS Spam-Ham Binary Classification

    Comparison of 20 ML + NLP Algorithms for SMS Spam-Ham Binary Classification

    This post analyzes a public-domain SMS text message dataset to compare various machine learning algorithms’ abilities to classify spam and ham messages. After implementing a Python workflow that includes data preparation, exploratory analysis, natural language processing, supervised machine learning binary classification, and a model performance analysis, the author finds that MLP, Logistic Regression CV, Linear…

  • Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%

    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…

  • Unsupervised ML, K-Means Clustering & Customer Segmentation

    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…

  • Dabl Auto EDA-ML

    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…

  • About Face Recognition ML Algorithms

    About Face Recognition ML Algorithms

    Facial Recognition (FR) involves mapping an individual’s facial features mathematically and storing the data as a faceprint. This case study outlines the process of Exploratory Data Analysis (EDA) and performance QC analysis for ML/AI workflows using public-domain datasets and real-time webcam GUI. The study includes the use of SVM for FR, dataset splitting, ML model…

  • Comparative ML/AI Performance Analysis of 13 Handwritten Digit Recognition (HDR) Scikit-Learn Algorithms with PCA+HPO

    Comparative ML/AI Performance Analysis of 13 Handwritten Digit Recognition (HDR) Scikit-Learn Algorithms with PCA+HPO

    Featured Photo by Torsten Dettlaff on Pexels Handwritten character recognition is one of the practically important issues in pattern recognition applications. Applications of digit recognition include national ID number recognition, postal mail sorting, automatic license plate recognition, bank check processing, form data entry, etc. In spite of recent advances in image recognition and computer vision technology, Handwritten…

  • The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    AI Health is leveraging Machine Learning (ML) and Artificial Intelligence (AI) for early diagnosis and prediction of breast cancer (BC), utilizing different ML techniques for binary classification of the disease. A comparative analysis demonstrated that Linear Regression was the most effective classifier based on various performance metrics. This research aims to integrate ML in public…

  • A Comparison of Scikit Learn Algorithms for Breast Cancer Classification – 2. Cross Validation vs Performance

    A Comparison of Scikit Learn Algorithms for Breast Cancer Classification – 2. Cross Validation vs Performance

    The post is a continuation of a previous breast cancer study comparing Scikit-Learn binary classifiers for cross validation and model performance. The classifiers compared include Logistic Regression, GaussianNB, SVC, KNN, Random Forest, Extra Trees, and Gradient Boosting. Learning curves show the comparison of classifier performance. Results indicate GaussianNB is more efficient than SVC in terms…