There are 4 repositories under random-forest-classifier topic.
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
Learning to create Machine Learning Algorithms
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
A machine learning project that predicts results of a football match
Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied
Final Year Project on Road Accident Prediction using user's Location,weather conditions by applying machine Learning concepts.
Detect Fraudulent Credit Card transactions using different Machine Learning models and compare performances
AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset
This project detects whether a news is fake or not using machine learning.
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.
A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.
An AI-driven platform offering crop recommendations, fertilizer suggestions, and disease detection for optimal farming
This is a Malware Detection ML model made using Random Forest Algorithm
Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.
A web app for Flight Delay Prediction using Random Forest Classifier
#FakersGonnaFake: using simple statistical tools and machine learning to audit instagram accounts for authenticity
A simple machine learning model for small-molecule target prediction in Python.
Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.
Technic analyzer with ML (RTF) and signal sender bot using telegram
Heart disease prediction using normal models and hybrid random forest linear model (HRFLM)
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
Recognition of Persomnality Types from Facebook status using Machine Learning
Similarity based email sorting for Google Mail using RandomForest classifiers
A random forest classifier to identify contigs of plasmid origin in contig and scaffold genomes
Finding which songs I like or not based on songs statistics
INSAID Assignment to create a ML model to detect fraud transactions for a financial company.
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.
IDS Alert Prioritization INSuRE Research Project
Implementation of various machine learning algorithms to predict the disease from symptoms.
Here are the codes for the "Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data" paper.
Implementation of a Random Forest classifier in both Python and Scala
Algerian Forest Fire Prediction
The purpose of this project is to be able to automatically and efficiently segment and classify high-grade and low-grade gliomas.
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.
A machine learning exercise using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.
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.