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.

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

ML/CNN Evaluation Metrics Package

An implementation of a density based outlier detection method - the Local Outlier Factor Technique, to find frauds in credit card transactions. For detecting both local and global outliers.

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

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.

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

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.

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.

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

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 .

Comprehensive Object-Oriented Programming Python implementation of a machine learning pipeline for diabetes prediction, featuring nested cross-validation, Bayesian hyperparameter optimization, and robust preprocessing for accurate and reliable outcomes.

Training binary classifier and multi-class classifier to classify the MNIST datase

A Comprehensive Guide to Titanic Machine Learning from Disaster

Light-weight package for classification metrics computed on streams or minibatches of data. Mainly for area under the curve (AUC) of precision-recall (PR) or receiver operating characteristic (ROC) curves. Supports multi-class setting with either macro- or micro aggregation..