The repository contains the code for the various machine learning algorithms used to make a predictive analysis of tweets on GST in India
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Updated
Mar 14, 2018 - Python
The repository contains the code for the various machine learning algorithms used to make a predictive analysis of tweets on GST in India
Toolkit for Doing Research with ECMAScript-based Statistics (DRESS Kit)
Resample precision-recall curves correctly!
Wolfram Language (aka Mathematica) paclet for Receiver Operation Characteristic (ROC) functions.
Explore how certain hyperparameters and features in a logistic regression model affect image classification
Human Resources Analytics
Classification-Techniques-For-Fraud-Detection
- Nesse trabalho vou explorar uma base vista em projetos passados, diabetes dataset. - Nela encontramos informações sobre algumas características de pacientes. Queremos estudar as características das pacientes e encontrar possíveis relações
Leveraging and comparing various ML techniques to forecast credit card defaults [Imbalanced data]
A Data Science Portfolio for potentially interested employers and recruiters.
[IEEE ATC 2017] "On the overall ROC of multistage systems". In IEEE International Conference on Advanced Technologies for Communications, 2017.
Credit card fraud detection based on the Kaggle dataset.
Using 21 predictor variables and applying simple Logistic Regression, predicting whether a particular customer will switch to another telecom provider or not. In telecom terminology, this is referred to as churning and not churning, respectively.
This project aims to predict the occurrence of diabetes using machine learning techniques. The dataset used for this analysis is the "diabetes_prediction_dataset.csv" file, which contains various features related to an individual's health condition.
Learning Machine Learning Through Data
This repository basically contains all the projects that I have carried out while learning Machine Learning on DataCamp.
Machine learning tutorial 'iris'
This notebook describes how to compute and derive insights from various classification evaluation metrics.
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