#Random-forest-classifier
Showing 12 of 12 repositories tagged #random-forest-classifier, ranked by stars
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset
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.
A machine learning web application use to predict chances of heart disease, built with FLASK and deployed on Heroku.
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.
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.
Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.
Technic analyzer with ML (RTF) and signal sender bot using telegram
Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
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 comprehensive machine learning project using Random Forest algorithm to predict wine quality based on physicochemical properties. Features EDA, model training, hyperparameter tuning, feature importance analysis, and detailed documentation.
A machine learning exercise using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.