Popular GitHub Repositories for Decision Tree Classifier

a chatbot based on sklearn where you can give a symptom and it will ask you questions and will tell you the details and give some advice.

Collection of various implementations and Codes in Machine Learning, Deep Learning and Computer Vision ??

Implementation of various machine learning algorithms to predict the disease from symptoms.

In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.

This project is on twitter sentimental analysis by combining lexicon based and machine learning approaches. A supervised lexicon-based approach for extracting sentiments from tweets was implemented. Various supervised machine learning approaches were tested using scikit-learn libraries in python and implemented Decision Trees and Naive Bayes techniques.

Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage

Text classification on job description dataset

Python implementation of basic machine learning algorithms

A prediction model that uses decision tree, logistic regression and artificial neural networks to diagnose the disease.

This repository shows the use of MLflow to track parameters, metrics and artifacts of a pipeline on a machine learning model.

The Repository describes the steps involved in performing a Named Entity Recognition Classifier using a Decision Tree approach.

A Parkinson’s detection model that uses voice and motor skill tests to predict the onset of Parkinson’s in individuals.

conducting the decision tree for capabilities of frameworks, that assist in reproducing results in Machine Learning: wandb, guild.ai and mlflow

Build a model to accurately predict whether the patients in the dataset have diabetes or not?

For this project I played around with and trained 2 machine learning algorithms to see which would classify the iris data set more accurately.

Worked on Machine learning algorithm using Decision Tree model and deployed model using Flask.

Classification and Regression problems

Practicum by Yandex Project 7: In this supervised learning project, we are to use customer data and develop a model that would predict whether a customer will leave a bank soon. One of the objective is to build a machine learning model with the maximum possible F1 score of atleast 0.59 or higher.