random-forest-classifier
There are 2689 repositories under random-forest-classifier topic.
x4nth055/emotion-recognition-using-speech
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
srafay/Machine_Learning_A-Z
Learning to create Machine Learning Algorithms
benedekrozemberczki/shapley
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
aziztitu/football-match-predictor
A machine learning project that predicts results of a football match
MrKhan0747/Diabetes-Prediction
Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied
Iretha/IoT23-network-traffic-anomalies-classification
AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset
kapilsinghnegi/Fake-News-Detection
This project detects whether a news is fake or not using machine learning.
theDefiBat/ROAD-ACCIDENTS-PREDICTION-AND-CLASSIFICATION
Final Year Project on Road Accident Prediction using user's Location,weather conditions by applying machine Learning concepts.
LaurentVeyssier/Credit-Card-fraud-detection-using-Machine-Learning
Detect Fraudulent Credit Card transactions using different Machine Learning models and compare performances
MohamedMostafa010/ExeRay
ExeRay AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.
asthasharma98/Heart-Disease-Prediction-Deployment
A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.
AnMol12499/CropcareAI
An AI-driven platform offering crop recommendations, fertilizer suggestions, and disease detection for optimal farming
Frightera/Sample-Machine-Learning-Projects
Some example projects that was made using Tensorflow (mostly). This repository contains the projects that I've experimented-tried when I was new in Deep Learning.
ravikant-diwakar/AgriSens
AI Powered Smart Farming Assistant uses advanced technology, including machine learning and CNNs, to provide farmers with crop recommendations, disease identification, weather forecasts, fertilizer recommendation, and crop management guidance through a user-friendly web app.
Kunal-Attri/Malware-Detection-ML-Model
This is a Malware Detection ML model made using Random Forest Algorithm
Soumilgit/XYZ-Bank-Customer-Churn-Predictor
Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.
Snigdho8869/Multiclass-Text-Classification
Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.
ikmckenz/target-pred-py
A simple machine learning model for small-molecule target prediction in Python.
MatteoM95/Default-of-Credit-Card-Clients-Dataset-Analisys
Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.
Devvrat53/Flight-Delay-Prediction
A web app for Flight Delay Prediction using Random Forest Classifier
athiyadeviyani/IGAudit
#FakersGonnaFake: using simple statistical tools and machine learning to audit instagram accounts for authenticity
elbernaderen/machine-learning-signal-finder
Technic analyzer with ML (RTF) and signal sender bot using telegram
VenkateshBH99/Hybrid-Random-Forest-Linear-Model
Heart disease prediction using normal models and hybrid random forest linear model (HRFLM)
jan-janssen/gmailsorter
Similarity based email sorting for Google Mail using RandomForest classifiers
kennedyCzar/URI-URL-CLASSIFICATION-USING-RECURRENT-NEURAL-NETWORK-SVM-AND-RANDOMFOREST
URI-URL Classification using Recurrent Neural Network, Support Vector and RandomForest. The Implementation results follows with classification report, confusion matrix and precision_recall_fscore_support for each validation result of a 10-fold crossval
leaemiliepradier/PlasForest
A random forest classifier to identify contigs of plasmid origin in contig and scaffold genomes
ashishrana1501/Forest-Fire-Prediction
Algerian Forest Fire Prediction
dayfundora/Personality-Type
Recognition of Persomnality Types from Facebook status using Machine Learning
sharmaroshan/Online-Shoppers-Purchasing-Intention
In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
tjnel/DSU_INSuRE_SP19_IDS_Prioritization
IDS Alert Prioritization INSuRE Research Project
Brice-Vergnou/spotify_recommendation
Finding which songs I like or not based on songs statistics
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.
Hongwei-Z/Federated-Random-Forest
Using Flower federated learning with scikit-learn random forest
parthsompura/Disease-prediction-using-Machine-Learning
Implementation of various machine learning algorithms to predict the disease from symptoms.
sauhard2701/Fraud-Transaction-Detection
INSAID Assignment to create a ML model to detect fraud transactions for a financial company.
KumudRanjan4295/Liver_Disease_Prediction_Using_Machine_Learning
This project leverages machine learning to predict liver disease using clinical data from the Indian Liver Patient Dataset. It combines exploratory data analysis (EDA), classification, and regression modeling to extract meaningful healthcare insights.