random-forest-classifier

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

A Chinese news headline classification benchmark: 200K labeled samples across 10 categories. Covers 4 approaches — TF-IDF+RandomForest, FastText, BERT fine-tuning+int8 quantization, and knowledge distillation (BERT→TextCNN) — spanning classical ML to model compression, with Flask RESTful API.

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.

This is a Malware Detection ML model made using Random Forest Algorithm

A simple machine learning model for small-molecule target prediction in Python.

A web app for Flight Delay Prediction using Random Forest Classifier

Technic analyzer with ML (RTF) and signal sender bot using telegram

A random forest classifier to identify contigs of plasmid origin in contig and scaffold genomes

Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learning

ML based Artificial Nose for spice identification using an array of 8 MOS gas sensors (MQ135, TGS series) with a PIC microcontroller. Implements Random Forest (100% accuracy) vs MLP comparison on 8135 sensor readings. IEEE conference paper, 2023.

Similarity based email sorting for Google Mail using RandomForest classifiers

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

Using Flower federated learning with scikit-learn random forest

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

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.