Dectecting chronic heart disease using machine learning 100%

SnapML library's Decision Tree classifier and SVM was used to train a model on a real dataset to identify fraudulent credit card transactions. The Decision Tree model resulted in ROC-AUC score = 0.92 and the SVM yielded ROC-AUC score = 0.93 and hinge loss = 0.15. Multi-threaded CPU was implemented to reduce model training time.

Recognising Handwritten digits with 98.89% accuracy, precision and f1-score

Recognizing handwritten digits with classical machine learning with a 97% accuracy and f1-score

Python scripts exploring machine learning algorithms with libraries like scikit-learn and snapml. Great for beginners!

Alphabet recognition

Prediction bankruptcy of Taiwanese bank in the years 1999 to 2009.

A decision tree regressor from SnapML by IBM was used to predict taxi tips on a dataset from the NYC TL Commission. The model was trained on over 3 million data samples in 0.636 seconds using multithreaded CPU/GPU acceleration. Achieved mean squared error = 1.62 on test data.

This repository contains a machine learning project focused on detecting credit card fraud using Decision Tree and Support Vector Machine (SVM) classifiers.

📚 Learn Scikit-learn basics to build and evaluate machine learning models efficiently in Python, with clear insights into algorithms and preprocessing techniques.

Detecting the fraudulent credit card transactions by training Decision Tree model using Scikit-learn and SnapML