Measure and visualize machine learning model performance without the usual boilerplate.

Anamoly Detection for Detecting Defected Manufactured Semi-Conductors, as in this case of Classification, the Defected Chips would be very less in comparison to perfect Chips so we have apply either Over-Sampling or Under-Sampling.

Machine learning utility functions and classes.

ML/CNN Evaluation Metrics Package

Matlab code for computing and visualization: Precision-Recall curve, AUPR, Accuracy etc. for Classification.

Script to compute Precision, Recall, AvP and MAP and to plot PR curves in the context of Information Retrieval evaluation.

Demonstrates the use of ML for Anomaly Detection for Credit Card Transactions: Identifying Fraudulent Activity using Imbalanced Data

This is an highly imbalanced data with only 1.72% minority and 98.28% majority class, i will be explaining Up and down sampling and effect of sampling before and while doing cross validation. Model has been evaluated using precision recall curve.

This code build up a predicting model use the Machine learning algorithms such as Decision Tree, k-Nearest Neighbors etc. on the Vehicle to predict the departure action

The project involves using machine learning techniques, like RandomForestClassifier and MLP, to predict whether a song will be popular or not based on its acoustic features. The input consists of various acoustic and metadata features, while the output is a binary classification.

A wide variety of supervised and unsupervised machine learning methods using the scikit-learn library

Predict fraudulent credit card transactions using TensorFlow, Keras, K Neighbors, Decision Tree, SVM Regression and Logistic Regression classifiers .

Identify which customer is willing to possess the insurance policy, so we campaign efficiently.

98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis.

Repository where it is intended to address the detection of patients who will be readmitted for problems with diabetes.

A credit risk text classification pipeline designed to simulate real-world modeling workflows. This project uses financial text data to predict borrower risk, incorporating data cleaning, NLP preprocessing, and model evaluation—emphasizing skills in feature engineering, model pipeline structuring, and explainable machine learning.

A Comprehensive Guide to Titanic Machine Learning from Disaster

Lightweight codebase for conducting precision-recall analysis for detectron2 models

Repository where it is intended to address the challenge of LATAM Airlines, which consists of predicting the probability of delay of flights that land or take off from the Santiago de Chile airport (SCL)