classification-report

Understanding emotions from audio files using neural networks and multiple datasets.

Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.

Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

A collection of statistical methods

The project aims to apply Naives Bayes on TF-IDF and Word2Vec Models .Use one of Selection Best Feature techniques to chose only features that contribute to the performance of the prediction

Sentiment analysis on customer reviews using machine learning and python

With the Student Alcohol Consumption data set, we predict high or low alcohol consumption of students.

Tool demonstrating building credit risk models

Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.

Faces recognition example using eigenfaces and SVMs

Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the performance of these models and made a recommendation on whether they should be used to predict credit risk.

Customer Retention Deep Learning Model

classify the Size_Categorie using SVM

Supervised Machine Learning and Credit Risk

Machine Learning Model in Jupyter notebook using Python on a Customer Satisfaction Data Set to explore, visualize, test, train and predict.

Faces recognition example using eigenfaces and SVMs

Evaluate Machine Learning Models with Yellowbrick

Linear Discriminant Analysis

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

This repo is about Machine Learning and Classification

Iris Data : Classification / Pattern Recognition, Predict the Class of Flower based on Available Attributes.

Belajar membuat Model Machine Learning untuk memprediksi apakah pengunjung baru website akan mengklik banner (ads)

Flexible utility functions for use with the sklearn library

predict where the patient will be discharged to before surgery

Here we will be firstly analysing the how different threshold values effect the area under the Curve in a Receiver Operating charcteristic(ROC) curve. And at last we will show how to define a function in python to calculate the most optimal threshold value for the logistic Regression.