To assess the impact of different stent designs on blood flow characteristics, focusing on parameters like Wall Shear Stress (WSS) and flow patterns

Heart disease remains a leading cause of mortality worldwide. Predictive modeling can assist in early detection and intervention. In this analysis, we aim to build a predictive model to classify the presence of heart disease based on patient health metrics.

This project estimates the Value at Risk (VaR) and Expected Shortfall (ES) of a financial portfolio using historical simulation, parametric methods, and Monte Carlo simulations. The analysis is performed on stock market data obtained from Yahoo Finance.

Author : Jebin Larosh Jervis

This notebook demonstrates a beginner-friendly time series analysis using the built-in AirPassengers dataset in R. We’ll explore the data, visualize trends and seasonality, check for stationarity, apply transformations, and fit an ARIMA model to forecast future values.

In this project, we will simulate genomic data and milk yield from dairy cows and build a machine learning model to predict milk yield based on genomic markers. The analysis will include feature selection, regression modeling, and visualization of prediction accuracy.

In this project, I applied advanced machine learning techniques to optimize oil well production. By simulating oil production data using scientifically plausible relationships between pressure, temperature, pump speed, and oil viscosity, I created a model that predicts and optimizes oil extraction rates. This project is designed to help oil companies like Sonatrach..etc.. improve their well productivity by adjusting key operational parameters.

In this project, we’ll analyze how study time and the school a student attends affect their exam performance. The key is to understand that some things (like study time) have a fixed influence on everyone, while other things (like the school a student attends) have a random influence because each school is different (teacher quality, school environment, etc.). REML helps us estimate how much random factors like the school a student attends affect their scores. Meanwhile, BLUE gives us the best estimate for how fixed factors like study time impact student performance.

In this project, we analyze the impact of sleep deprivation on reaction time using REML (Restricted Maximum Likelihood) and BLUE (Best Linear Unbiased Estimators). We use the sleepstudy dataset which contains reaction time measurements of participants across several days of sleep deprivation. By using a mixed-effects model, we aim to account for individual-level differences (random effects) and the effects of sleep deprivation on reaction time (fixed effects).

The goal is to estimate milk yield in dairy cows using REML to fit a mixed-effects model that accounts for both fixed and random effects, and BLUE for the fixed-effect estimates. The data will consist of feed intake, milk yield, and cow-specific factors (breed, age, lactation stage), and we'll use REML to model the random effect of cow-to-cow variability in milk production.

This project focuses on analyzing the relationship between feed intake (protein, fat, and energy) and milk yield in dairy cows using a combination of mathematical models, linear algebra, and statistical techniques. The analysis demonstrates how feed nutrients affect milk production and provides insights into feed efficiency. The project uses synthetic data to simulate real-world scenarios and applies linear regression, PCA, ANOVA, and matrix operations to explore and predict milk yield.