The practitioner's forecasting library

🔉 👦 👧Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)

A fast, robust library to check for offensive language in strings, dropdown replacement of "profanity-check".

🔉 👦 👧 👩 👨 Speaker identification using voice MFCCs and GMM

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.

Recognition of the images with artificial intelligence includes train and tests based on Python.

Efficient sparse matrix implementation for various "Principal Component Analysis"

Octopi ML API is a Flask-based backend service hosting a machine learning model trained on over 500 software projects to predict development effort. It exposes RESTful endpoints for real-time effort estimation (in person-hours) and facilitates seamless integration with the Octopi frontend.

The project scope is a weather forecasting model based on behavioral analysis of the last 33 hours (hour-by-hour forecast) with Random Forest Classifier. The program automatically saves and loads the last trained model for prediction.

skfeaturellm is a Python library that brings the power of Large Language Models (LLMs) to feature engineering for tabular data, wrapped in a familiar scikit-learn–style API.

Simple Python scripts that help automate and simplify tasks.

Crowd monitoring and cluster detection system with heatmap using Python & OpenCV

DMLLTDetectorPulseDiscriminator - A supervised machine learning approach for shape-sensitive detector pulse discrimination in lifetime spectroscopy applications

Basic federated learning pipeline using Flower and scikit‑learn on autism dataset.

ML Python - Logistic Regression (GLM)

ML-powered phishing URL detection system — Random Forest, Gradient Boosting, SVM & Logistic Regression with a cyberpunk Flask web app

This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.

analysis application developed with Streamlit, designed to facilitate the evaluation of review sentiments—positive or negative—by leveraging a pre-trained machine learning model. Users can input review text and instantly receive sentiment predictions through a streamlined, interactive web interface powered by Streamlit .

Codes for "Parkinson’s Disease Diagnosis: Effect of Autoencoders to Extract Features from Vocal Characteristics"

a python project that uses machine learning to estimate the weight of a fish.