Fundamentals of Machine Learning Assignment Repository

Yeo-Johnson transformed distributions in PyTorch

We leverage Databricks' MLLib and PySpark libraries to predict continuous outliers using time-series flight delays, NOAA, and geospatial data. We use Yeo-Johnson, Euclidean distance, and custom joins to build a robust dataset—prior to model training. Work completed as part of ML at Scale, DATASCI 261.

Regression models determining the socioeconomic factors impacting "Generosity" within a given country

A Python-based utility for testing and visualizing data transformations. Includes normality tests (Shapiro-Wilk and Lilliefors), a variety of transformation methods, and visual analysis tools for histogram and Q-Q plots.

Fortran functions for variable transformation

ML Project ,XGboost .Logistic Regression as classification,Decision Tree & balancing technique Undersampling & SMOTE.

Underwater Minesweeper : Mine Classification Using SONAR Data

Fitting a support vector classifier to the Kepler Exoplanet Search data set.

Power Transformer works best on linear model and The Power Transformer actually automates this decision making by introducing a parameter called lambda. It decides on a generalized power transform by finding the best value of lambda

Who Are Your Real Customers? RFM Modelling & K-Means Reveal the Answer.

Cluster analysis of population based on demographic and health data. Clusters then used to determine which have a higher instance of diabetes.

A/B testing impact of progression system changes on player retention / interaction. Non-parametric hypothesis testing and power transformations for non-normally distributed data.

A Python-based utility for testing and visualizing data transformations. Includes normality tests (Shapiro-Wilk and Lilliefors), a variety of transformation methods, and visual analysis tools for histogram and Q-Q plots.