decision-tree-classifier | Topic

Topic: "decision-tree-classifier"

benedekrozemberczki/awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

Language: Python - Size: 870 KB - Last synced at: 4 days ago - Pushed at: about 1 year ago - Stars: 2,399 - Forks: 343

srafay/Machine_Learning_A-Z

Learning to create Machine Learning Algorithms

Language: Python - Size: 10.8 MB - Last synced at: 5 days ago - Pushed at: almost 4 years ago - Stars: 388 - Forks: 195

Suji04/ML_from_Scratch

Implementation of basic ML algorithms from scratch in python...

Language: Jupyter Notebook - Size: 540 KB - Last synced at: about 1 month ago - Pushed at: about 4 years ago - Stars: 293 - Forks: 229

milaan9/Python_Decision_Tree_and_Random_Forest

I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.

Language: Jupyter Notebook - Size: 3.9 MB - Last synced at: 7 days ago - Pushed at: over 2 years ago - Stars: 262 - Forks: 203

moon-hotel/MachineLearningWithMe

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见10余种机器学习算法原理与实现及视频讲解。

Language: Jupyter Notebook - Size: 35.7 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 254 - Forks: 47

itachi9604/healthcare-chatbot

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.

Language: Python - Size: 64.5 KB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 240 - Forks: 156

sharmapratik88/AIML-Projects

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

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theDefiBat/ROAD-ACCIDENTS-PREDICTION-AND-CLASSIFICATION

Final Year Project on Road Accident Prediction using user's Location,weather conditions by applying machine Learning concepts.

Language: HTML - Size: 8.17 MB - Last synced at: about 2 months ago - Pushed at: over 5 years ago - Stars: 79 - Forks: 36

HarshCasper/Brihaspati

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

Language: Jupyter Notebook - Size: 140 MB - Last synced at: about 1 month ago - Pushed at: about 3 years ago - Stars: 77 - Forks: 29

thieu1995/mafese

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

Language: Python - Size: 4.51 MB - Last synced at: 29 days ago - Pushed at: 11 months ago - Stars: 72 - Forks: 24

appleyuchi/Decision_Tree_Prune

Decision Tree with PEP,MEP,EBP,CVP,REP,CCP,ECP pruning algorithms,all are implemented with Python(sklearn-decision-tree-prune included,All are finished).

Language: C - Size: 30.6 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 63 - Forks: 34

kapilsinghnegi/Fake-News-Detection

This project detects whether a news is fake or not using machine learning.

Language: Jupyter Notebook - Size: 42.9 MB - Last synced at: about 1 month ago - Pushed at: 2 months ago - Stars: 58 - Forks: 35

abhinav-bhardwaj/IoT-Network-Intrusion-Detection-System-UNSW-NB15

Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset

Language: Jupyter Notebook - Size: 24.5 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 43 - Forks: 23

starkblaze01/Artificial-Intelligence-Codes

Collection of Artificial Intelligence Algorithms implemented on various problems

Language: Jupyter Notebook - Size: 5.88 MB - Last synced at: 22 days ago - Pushed at: over 4 years ago - Stars: 41 - Forks: 9

joachimvalente/decision-tree-cart

Simple implementation of CART algorithm to train decision trees

Language: Python - Size: 18.6 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 35 - Forks: 29

rohit1576/Decision-Tree

Python implementation of Decision trees using ID3 algorithm

Language: Python - Size: 2.93 KB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 34 - Forks: 62

greenfish77/gaenari

c++ incremental decision tree

Language: C++ - Size: 707 KB - Last synced at: 12 months ago - Pushed at: almost 3 years ago - Stars: 26 - Forks: 2

IndexFziQ/ML-ATIC

Abnormal Traffic Identification Classifier based on Machine Learning. My code for undergraduate graduation design.

Language: Java - Size: 45.8 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 26 - Forks: 10

LaurentVeyssier/Credit-Card-fraud-detection-using-Machine-Learning

Detect Fraudulent Credit Card transactions using different Machine Learning models and compare performances

Language: Jupyter Notebook - Size: 57.4 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 26 - Forks: 19

vaishnavipatil29/Career-Guidance-ML-Project

Career Guidance System Using Machine Learning Techniques

Language: Jupyter Notebook - Size: 2.85 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 25 - Forks: 10

Iretha/IoT23-network-traffic-anomalies-classification

AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset

Language: Python - Size: 21.4 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 24 - Forks: 6

Yeaseen/ML_Pattern

:trident: Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.

Language: Python - Size: 6.44 MB - Last synced at: 4 days ago - Pushed at: about 6 years ago - Stars: 23 - Forks: 3

yogeshwaran-shanmuganathan/Airline-Passenger-Satisfaction

Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.

Language: Jupyter Notebook - Size: 3.97 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 22 - Forks: 14

shrebox/Political-Ideology-Detection-on-Twitter

Predicting Political Ideology of Twitter Users.

Language: Python - Size: 11 MB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 22 - Forks: 2

harshitahluwalia7895/Loan_Prediction

Language: Jupyter Notebook - Size: 289 KB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 22 - Forks: 27

thieu1995/MHA-FS 📦

The biggest module developed with complete focus on Feature Selection (FS) using Meta-Heuristic Algorithm / Nature-inspired evolutionary / Swarm-based computing.

Language: Python - Size: 2.51 MB - Last synced at: 8 months ago - Pushed at: almost 2 years ago - Stars: 21 - Forks: 7

srijarkoroy/adaboost

An implementation of the paper "A Short Introduction to Boosting"

Language: Python - Size: 1.11 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 19 - Forks: 6

blhprasanna99/speech_emotion_detection

Speech_Emotion_detection-SVM,RF,DT,MLP

Language: Jupyter Notebook - Size: 623 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 18 - Forks: 1

innat/Py4-DS

:snake: Data Science Boot-Camp : UC San DiegoX

Language: Jupyter Notebook - Size: 33.9 MB - Last synced at: 19 days ago - Pushed at: over 1 year ago - Stars: 17 - Forks: 17

danwild/decision-tree-builder

Build serialisable flowchart-style decision trees with D3.

Language: JavaScript - Size: 576 KB - Last synced at: 19 days ago - Pushed at: over 5 years ago - Stars: 17 - Forks: 6

athiyadeviyani/IGAudit

#FakersGonnaFake: using simple statistical tools and machine learning to audit instagram accounts for authenticity

Language: Jupyter Notebook - Size: 2.77 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 16 - Forks: 3

rsher60/Sentiment-Analysis-by-combining-Machine-Learning-and-Lexicon-Based-methods

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.

Language: Python - Size: 6.36 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 16 - Forks: 4

VenkateshBH99/Hybrid-Random-Forest-Linear-Model

Heart disease prediction using normal models and hybrid random forest linear model (HRFLM)

Language: Python - Size: 14.6 KB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 15 - Forks: 22

ksdkamesh99/Spam-Classifier

A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.84%.

Language: Jupyter Notebook - Size: 510 KB - Last synced at: 21 days ago - Pushed at: over 4 years ago - Stars: 15 - Forks: 11

tjnel/DSU_INSuRE_SP19_IDS_Prioritization

IDS Alert Prioritization INSuRE Research Project

Language: Jupyter Notebook - Size: 19.5 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 15 - Forks: 6

parthsompura/Disease-prediction-using-Machine-Learning

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

Language: Python - Size: 51.8 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 14 - Forks: 6

Niranjankumar-c/CreditRiskAnalytics

Predicting the default customers

Language: Jupyter Notebook - Size: 2.78 MB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 14 - Forks: 23

StarlangSoftware/Classification-Py

Machine learning library for classification tasks

Language: Python - Size: 1.4 MB - Last synced at: 14 days ago - Pushed at: 5 months ago - Stars: 13 - Forks: 2

kanchitank/Mushroom-Classification

A machine-learning project to determine if a certain mushroom is edible or poisonous.

Language: Jupyter Notebook - Size: 14 MB - Last synced at: 4 days ago - Pushed at: about 4 years ago - Stars: 13 - Forks: 13

mehtabhavin10/insurance_fraud_detection

:mag: | :chart_with_upwards_trend: | Life / Health Insurance Fraud Detection | :clipboard: | (Codeshahstra Round 1 Hackathon)

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ritvikkhanna09/Income-Prediction-Webapp

Flask based Web application for predicting the income of a person

Language: Jupyter Notebook - Size: 641 KB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 13 - Forks: 29

Dalageo/ML-TitanicShipwreck

Exploring the World's Most Renowned Shipwreck 🚢

Language: Jupyter Notebook - Size: 990 KB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 12 - Forks: 2

akbloodadarsh/Twitter-Sentimental-Analysis

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.

Language: Python - Size: 93.8 KB - Last synced at: 6 months ago - Pushed at: about 2 years ago - Stars: 12 - Forks: 12

bharathsudharsan/ML-Classifiers-on-MCUs

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'

Language: Jupyter Notebook - Size: 584 KB - Last synced at: 18 days ago - Pushed at: almost 4 years ago - Stars: 12 - Forks: 1

parthk3004/Quizzaro

Quizzaro The Personality Quiz

Language: Python - Size: 47.9 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 12 - Forks: 6

teddyoweh/Titanic-Decision-Tree-Classifier

Decision Tree Classifier on the Titanic Dataset to determine if a person survived

Language: Jupyter Notebook - Size: 24.4 KB - Last synced at: about 1 month ago - Pushed at: about 3 years ago - Stars: 11 - Forks: 0

SohelRaja/Customer-Churn-Analysis

Implementation of Decision Tree Classifier, Esemble Learning, Association Rule Mining and Clustering models(Kmodes & Kprototypes) for Customer attrition analysis of telecommunication company to identify the cause and conditions of the churn.

Language: Jupyter Notebook - Size: 15.3 MB - Last synced at: 5 months ago - Pushed at: over 5 years ago - Stars: 11 - Forks: 13

ritabratamaiti/Blooddonorprediction

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 of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

Language: Python - Size: 2.22 MB - Last synced at: 5 days ago - Pushed at: almost 7 years ago - Stars: 11 - Forks: 2

nehal96/Machine-Learning-Enron-Fraud

Using Machine Learning to Identify Fraud in the Enron Corpus

Language: Python - Size: 444 KB - Last synced at: about 2 years ago - Pushed at: over 8 years ago - Stars: 11 - Forks: 5

somjit101/Facebook-Friend-Recommendation

This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media platforms and a directed edges (or 'links') indicates that one person 'follows' the other, or are 'friends' on social media. Now, the task is to predict newer edges to be offered as 'friend suggestions'.

Language: Jupyter Notebook - Size: 770 KB - Last synced at: 20 days ago - Pushed at: about 1 year ago - Stars: 10 - Forks: 3

Owinnie/Data-Science-Lab

Learning Data Science

Language: Python - Size: 147 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 10 - Forks: 5

aj1365/DeepForest-Wetland-Paper

Here are the codes for the "Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data" paper.

Language: Jupyter Notebook - Size: 70.3 KB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 10 - Forks: 1

krunal-nagda/Credit-Card-Fraud-Detection-Capstone-Project---Decision-Tree-and-Random-Forest

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.

Language: Jupyter Notebook - Size: 214 KB - Last synced at: 10 months ago - Pushed at: almost 4 years ago - Stars: 10 - Forks: 9

MarAl15/DiabeticRetinopathyDetection

Diagnosis of diabetic retinopathy from fundus images using SVM and decision trees.

Language: MATLAB - Size: 15.2 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 10 - Forks: 4

iamjagdeesh/Gesture-Recognition-System-for-American-Sign-Language

A system to recognize hand gestures by applying feature extraction, feature selection (PCA) and classification (SVM, decision tree, Neural Network) on the raw data captured by the sensors while performing the gestures.

Language: MATLAB - Size: 75.6 MB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 10 - Forks: 9

rebeccak1/conversion_rates

Predict conversion rate and generate ideas to improve conversion rate

Language: Jupyter Notebook - Size: 1.64 MB - Last synced at: 8 months ago - Pushed at: over 7 years ago - Stars: 10 - Forks: 4

AbdullahShiraz/AYUCARE

AYUCARE is a web application that provides a solution to detect diseases from symptoms and recommend Ayurvedic medicine using two machine learning models that are based on decision tree algorithms.

Language: Jupyter Notebook - Size: 13.8 MB - Last synced at: 3 months ago - Pushed at: about 2 years ago - Stars: 9 - Forks: 11

emirhanai/Machine-Learning-Software-that-predicts-planets-based-on-their-distance-from-the-sun-number-of-sate

Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties

Language: Python - Size: 1.02 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 9 - Forks: 1

anshul1004/DecisionTree

Decision Tree classifier from scratch without any machine learning libraries

Language: Python - Size: 24.4 KB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 9 - Forks: 6

subhadeep-123/Breast-Cancer-Detection

Breast Cancer Wisconsin (Diagnostic) Prediction Using Various Architecture, though XgBoost Classifier out performed all

Language: Jupyter Notebook - Size: 390 KB - Last synced at: about 1 year ago - Pushed at: almost 6 years ago - Stars: 9 - Forks: 9

AntonSarr/strtree

Python package for strings binary classification, based on trees and regular expressions

Language: JavaScript - Size: 708 KB - Last synced at: 25 days ago - Pushed at: 10 months ago - Stars: 8 - Forks: 0

aryanraj2713/Imdb-movie-review-analysis-using-NLP

Language: Jupyter Notebook - Size: 3.2 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 8 - Forks: 1

leihao1/Comparison-of-Machine-Learning-Prediction-Models

Compared different classification and regreesion models performance in scikit-learn by applying them on 20 datasets from UCL website.

Language: Jupyter Notebook - Size: 237 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 8 - Forks: 5

bhattbhavesh91/visualize-decision-tree

🌲 Decision Tree Visualization using GraphViz and Python

Language: Jupyter Notebook - Size: 130 KB - Last synced at: 23 days ago - Pushed at: almost 4 years ago - Stars: 8 - Forks: 35

sushant1827/Human-Activity-Recognition-with-Smartphones

Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.

Language: Jupyter Notebook - Size: 46.4 MB - Last synced at: about 1 month ago - Pushed at: over 5 years ago - Stars: 8 - Forks: 3

arnab39/Machine-Learning-Codes

Python implementation of basic machine learning algorithms

Language: Python - Size: 58.6 KB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 8 - Forks: 8

StarlangSoftware/Classification

Machine learning library for classification tasks

Language: Java - Size: 2 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 7 - Forks: 6

degerliaysen/MultiEchoAI

Myocardial Infarction Detection

Language: Python - Size: 1 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 7 - Forks: 3

rylp/LP3

SPPU BE-SEM2 LP3 All Codes

Language: Python - Size: 1.55 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 0

RRaphaell/MyFitnessPal

Language: Jupyter Notebook - Size: 43.3 MB - Last synced at: almost 2 years ago - Pushed at: about 3 years ago - Stars: 7 - Forks: 4

jaykay12/CricAI

Desktop App & Web App for Cricket Outcome Prediction.

Language: Python - Size: 8.54 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 7 - Forks: 26

FlorentAvellaneda/InferDT

The code of AAAI20 paper "Efficient Inference of Optimal Decision Trees"

Language: C++ - Size: 1.18 MB - Last synced at: 6 months ago - Pushed at: almost 5 years ago - Stars: 7 - Forks: 5

vedantpuri/emoji-prediction

Predicts an emoji based on a given tweet using various NLP models.

Language: Python - Size: 7.35 MB - Last synced at: about 2 years ago - Pushed at: almost 7 years ago - Stars: 7 - Forks: 5

mahesh147/Decision-Tree-Classifier

Decision Tree Classifier model implemented in a python program.

Language: Python - Size: 3.91 KB - Last synced at: about 2 years ago - Pushed at: over 7 years ago - Stars: 7 - Forks: 16

haroldeustaquio/Data-Mining-UNAM

This repository showcases projects from the Data Mining course at UNAM, Mexico. It includes analyses of customer behavior, sales transactions, and a sequence-to-sequence model for text generation based on the Harry Potter series, all developed and presented throughout the semester.

Language: Jupyter Notebook - Size: 11.2 MB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 6 - Forks: 2

Ruban2205/Data-Science-Introduction

Welcome to the Data Science Introduction repository! This repository is designed to provide an introduction to the field of data science, covering various topics and techniques commonly used in the industry.

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novandikp/DecisionTreeC45

DecisionTreeC45 is a library for creating decision trees using the C4.5 algorithm.

Language: Python - Size: 38.1 KB - Last synced at: 22 days ago - Pushed at: almost 2 years ago - Stars: 6 - Forks: 1

NarutoOp/Crop-Recommendation

Crop recommend System on the basis of location .

Language: Jupyter Notebook - Size: 24.4 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 6 - Forks: 4

rochitasundar/Customer-profiling-using-ML-EasyVisa

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidates more likely to have the visa certified.

Language: Jupyter Notebook - Size: 7.68 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 2

emirhanai/Ozone-Day-AdaBoostClassifier-and-Random-Forest-Tree-Classifier-with-Machine-Learning

Ozone Day AdaBoostClassifier and Random Forest Tree Classifier with Machine Learning

Language: Python - Size: 780 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 0

ferdouszislam/Android-Malware-Detection-ML

Android malware detection from static features with ML classification algorithms- Decision Tree, K-Nearest Neighbours, Linear SVM and Random Forest.

Language: Jupyter Notebook - Size: 19.1 MB - Last synced at: 3 months ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 5

shanathvemula/framework_for_superwised_prediction2

Language: JavaScript - Size: 2.12 MB - Last synced at: about 1 month ago - Pushed at: almost 4 years ago - Stars: 6 - Forks: 0

romulogmlima/ML-letter-recognition

Applying Machine Learning techniques to identify randomly distorted capital letters in the English alphabet.

Language: Python - Size: 550 KB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 6 - Forks: 1

sahirnoorali/poker-hand-analysis

Analysis and Prediction of Poker Hand Strength

Language: Python - Size: 849 KB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 6 - Forks: 4

kevalmorabia97/ID3-Decision-Tree-Classifier-in-Java

ID3 Decision Tree Classifier for Machine Learning along with Reduced Error Pruning and Random Forest to avoid overfitting

Language: Java - Size: 1.46 MB - Last synced at: about 1 month ago - Pushed at: over 7 years ago - Stars: 6 - Forks: 3

StarlangSoftware/Classification-CPP

Machine learning library for classification tasks

Language: C++ - Size: 64.1 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 5 - Forks: 0

IsaacCheng9/machine-learning-in-chess

My final year project for the University of Exeter, using machine learning to study patterns in millions of chess games (~350 GB). Ranked 1st in the cohort for undergraduate projects (85%).

Language: Jupyter Notebook - Size: 1.27 GB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 5 - Forks: 1

flowa-ai/flowa

Machine Learning Toolkit

Language: Python - Size: 290 KB - Last synced at: 9 months ago - Pushed at: over 1 year ago - Stars: 5 - Forks: 1

KhaledAshrafH/DT-Banknote-Authenticator

This Python code utilizes the decision tree algorithm from the scikit-learn library to perform banknote authentication. The code aims to analyze the impact of different train-test split ratios and training set sizes on the accuracy and size of the learned decision tree.

Language: Python - Size: 107 KB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 5 - Forks: 0

nano-bot01/Heart-Disease-Prediction-System-using-Machine-Learning

A Heart Disease Prediction System built on machine learning

Language: Jupyter Notebook - Size: 3.31 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 5 - Forks: 0

brenomfviana/pope

Pope is a bot created to play a simplified version of the game "Papers, Please". The name of this AI was given in honor of the game creator Lucas Pope.

Language: C++ - Size: 630 KB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 5 - Forks: 3

ChaitanyaC22/Fraud_Analytics_Credit_Card_Fraud_Detection

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

Language: Jupyter Notebook - Size: 67.3 MB - Last synced at: 28 days ago - Pushed at: over 2 years ago - Stars: 5 - Forks: 2

vamc-stash/Decision-Tree

Implements Decision tree classification and regression algorithm from scratch in Python.

Language: Python - Size: 44.6 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 5 - Forks: 4

emykes/Flu_Vaccination_ML

The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to systematically act on those features hindering people to get vaccinated.

Language: Jupyter Notebook - Size: 4.92 MB - Last synced at: 7 months ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 1

shanathvemula/framework_for_superwised_prediction

This is the framework for supervised algorithms in mechine learning

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Abdulrehman786302/Cell-Phone-detection-in-restricted-areas

The objective of this work is to detect the cell phone and/or camera used by a person in restricted areas. The paper is based on intensive image processing techniques, such as, features extraction and image classification. The dataset of images is generated with cell phone camera including positive (with cell phone) and negative (without cell phone) images. We then extract relevant features by using classical features extraction techniques including Histogram of Oriented Gradients (HOG) and Speeded up Robust Features (SURF).The extracted features are then, passed to classifier for detection. We employ Support Vector Machine (SVM), Nearest Neighbor (K-NN) and Decision tree classifier which are already trained on our dataset of training images of persons using mobile or otherwise. Finally, the detection performance in terms of error rate is compared for various combinations of feature extraction and classification techniques. Our results show that SURF with SVM classifier gives the best accuracy.

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