🧠 Build and run perceptive-language models with this Python SDK and CLI, ensuring stability across updates and flexibility in provider selection.

The goal of this task is to perform the initial "data discovery" phase of a Machine Learning workflow. This involves loading raw data, inspecting its structure, and using statistical visualizations to identify patterns, correlations, and outliers.

We can use the decision tree model to understand the relationship between the target variable and the input variable.

Machine learning classification project developed during the Unified Mentor internship to classify Iris flowers into species using supervised learning and model evaluation techniques.

Automated ML deployment pipeline for Iris classification using FastAPI, Docker, and GCP Cloud Build with CI/CD

A project to train and evaluate a model using Support Vector Machine (SVM). The aim is to classify (predict) the iris species into one of the three categories

Iris dataset EDA and classification model comparison

Exploratory Data Analysis (EDA) on the classic Iris dataset using Python. Includes data quality checks, descriptive statistics, visualizations, and species-wise feature analysis.

A machine learning project that predicts Iris flower species using Logistic Regression. The model is trained on the Iris dataset, serialized with Joblib, and deployed as an interactive Streamlit web application.

A scalable multi-layer perceptron (MLP) implementation for the Iris dataset, supporting both classification and regression with PyTorch.

Classify iris flowers in real time with a PyTorch model and see predictions on an Arduino LED matrix by entering measurements via the web interface.

Pipeline básico de Machine Learning para clasificación de flores Iris usando Regresión Logística.

Materiali del corso di Machine Learning del dottorato in Digital Humanities dell'Università Telematica Pegaso

using iris dataset to create a neural network with pytorch

Implementação da rede perceptron utilizando Numpy puro em paralelo com o paradigma orientado a objetos, aplicando a técnica do One vs Rest para resolver o problema de classificação multiclasse do dataset iris.

This project was developed during an AI/ML internship to apply core machine learning concepts using Python. The objective was to implement an Iris Flower Classification model using the KNN algorithm, covering data preprocessing, feature scaling, and model evaluation.

a simple implementation of a Logistic Regression model to classify the famous Iris flower dataset.

A comprehensive machine learning project for classifying Iris flower species using multiple classification algorithms. This project includes exploratory data analysis (EDA), model comparison, and an interactive Power BI dashboard.

[French] Projet de Clustering de données [English] Data Clustering Project

Parzen window classifier algorithm (https://en.wikipedia.org/wiki/Kernel_density_estimation#Statistical_implementation) applied to the Iris dataset (https://archive.ics.uci.edu/dataset/53/iris); Assignment #1 of CS 7063 - Advanced Machine Learning, Spring 2026 - University of Cincinnati