There are 1 repository under iris-flower-classification topic.
This project is for the Identification of Iris flower species is presented
Simple Classification program to predict the species of an iris flower.
AI Nexus π is a streamlined suite of AI-powered apps built with Streamlit. It features π StyleScan for fashion classification, π©Ί GlycoTrack for diabetes prediction, π’ DigitSense for digit recognition, πΈ IrisWise for iris species identification, π― ObjexVision for object recognition, and π GradeCast for GPA prediction with detailed insights.
Iris flower classification with MLP using MATLAB.
An application for beginners of Machine Learning for understanding Machine Learning basic concepts.
This Repository Consists of all the tasks that were assigned to me during internship at Oasis Infobyte as a Data Science Intern from October 15th 2023 to November 15th 2023
The "IRIS Flower Classification" GitHub repository is a project dedicated to classifying iris flowers based on their attributes.
This project uses the K-Nearest Neighbors (KNN) algorithm to classify Iris flowers based on their sepal and petal measurements. The dataset used in this project is the Iris Dataset, which includes 150 samples of Iris flowers, each with four features: sepal length, sepal width, petal length, and petal width.
In this repository, I have done simple python projects for understanding the python environment.
Data Science Intern @LetsGrowMore Foundation LGMVIP October-21
A machine-learning project that classifies Iris Flowers based on certain characteristics (Training + Deployment)
πΌ Classify the different species of the Iris flower.
Implementing all ML models and feature selection techniques that can be used.
Sparks foundation Data analytics task#6 Prediction using decision tree algorithm on the Iris dataset
Learning with sklearn diabetes and iris flower dataset, single and multiple linear regression, classification with multi-layer perceptron, kneighbors and support vector machines.
A ML project on the classification of the Iris dataset, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.
The "Iris-Flower-Classifier" is a machine learning project that categorizes Iris flowers into three species based on their measurements. It involves data preprocessing, model training, and evaluation, showcasing a fundamental classification task.
This is basic Machine Learning project of Iris Flower Classification
Projects
This repository comprises three distinct machine learning projects :Titanic Survival Prediction, Movie Rating Prediction, Iris Flower Classification,
Tasks and Projects completed during the Data Science internship at CODSOFT
A repository containing all my tasks/projects during my internship at CognoRise Infotech
The iris dataset contains 4 features which i used to classify the flowers into one of the three species based on the measurements.
I am currently pursuing an internship where I am honing my skills in data science and machine learning. My passion lies in uncovering insights from data and building predictive models that can drive meaningful impact.
Data Science Internship at CodSoft
Data Science Internship at Oasis InfoByte July - August 2023
I recently completed my five distinct tasks, as part of my data science and machine learning internship at Oasis infobyte.
Project Portfolio Exploratory Data Analysis EDA is Exploratory and Explanatory Data Analysis. I'm using iris dataset from sklearn. I'm using The dataset from this link : https://scikit-learn.org/1.5/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris
A web application for predicting Iris flower species using machine learning models with Gradio UI. Users can input measurements and receive predictions along with a representative image.
Projects on Machine Learning Internship
Iris flower classification using KNN and Random forest algorithm
The goal of this project is to develop a machine learning model for the classification of Iris flowers based on their sepal and petal measurements. The dataset used for this task is the well-known Iris dataset, which includes features such as sepal length, sepal width, petal length, and petal width.