Tag: training data

  • 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…

  • 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…

  • SARIMAX Crude Oil Prices Forecast – 2. Brent

    SARIMAX Crude Oil Prices Forecast – 2. Brent

    This study focuses on validating the EIA energy forecast for the 2023 Brent crude oil spot price using SARIMAX time-series cross-validation. It includes prerequisites, data loading, ETS decomposition, ADF test, SARIMAX modeling, predictions, model evaluation, and summary. The predictions align with the EIA forecast, with discrepancies within predicted confidence intervals.

  • SARIMAX Forecasting of Online Food Delivery Sales

    SARIMAX Forecasting of Online Food Delivery Sales

    This article provides a beginner-friendly guide to understanding and evaluating ARIMA-based time-series forecasting models such as SARIMA and SARIMAX. It focuses on an QC-optimized SARIMA(X) model to forecast the e-commerce sales of a food delivery company. The post covers essential concepts, data processing, model comparisons, and insights. It also includes a comparison between SARIMA and…

  • Supervised Machine Learning Use Case: Prediction of House Prices

    This is the application of supervised machine learning to real estate. The goal is to predict sale prices ($) for N selected properties in a state (N>>1000).  We are given a csv dataset as a NxM table, where M is the number of property features describing every aspect of the house and surroundings (typically, M<100).   …

  • About MLOps

    About MLOps

    Machine Learning (ML), a subset of Artificial Intelligence, enables computers to learn from experience, improving tasks through performance measures. Deployed by businesses across sectors, ML powers various applications such as chatbots, decision support tools, fraud detection, etc. ML uses data analytics concepts like predictive and prescriptive algorithms, and techniques such as supervised, unsupervised, and deep…