Tag: Kaggle
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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…
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Walmart Weekly Sales Time Series Forecasting using SARIMAX & ML Models

The blog post delves into Time Series Forecasting (TSF), using SARIMAX and Supervised Machine Learning algorithms to predict Walmart’s weekly store sales. Factors affecting sales are investigated for strategies to increase revenues. The study additionally covers data preparation, feature correlation analysis, SARIMAX diagnostics, and the training of supervised ML models like Linear Regression, Random Forest,…
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90% ACC Diabetes-2 ML Binary Classifier

A study aims to develop an ML-driven e-diagnosis system for detecting and classifying Type 2 Diabetes as an IoMT application. By leveraging advanced supervised ML algorithms, the system can predict a person’s diabetes risk based on several factors, provide a preliminary diagnosis, and relay doctor’s guidance on diet, exercise, and blood glucose testing. The Pima…
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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…
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AI-Powered Stroke Prediction
Contents: Introduction Stroke, also known as brain attack, happens when blood flow to the brain is blocked, preventing it from getting oxygen and nutrients from it and causing the death of brain cells within minutes. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally after ischemic heart disease,…
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Heart Failure Prediction using Supervised ML/AI Technique

Introduction This project is aimed to support ESC guidelines [1] that help health professionals manage people with heart failure (HF) according to the best available evidence. The objective is not only to develop an accurate survival prediction model but also to discover essential factors for the survival prediction of HF patients. The complex nature of HF produces a significant amount…