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.

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

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.

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 .

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

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.

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.

Linear Regression, Logistic Regression, ML Pipeline

The company has collected historical customer and claims data and wants to use it to develop a machine learning model that can predict whether a customer will file an insurance claim in the next year.

Results of binary classification of Yelp reviews as pertaining to conventional or alternative medicine using random forests