Nandinee Kasar’s Post

Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according to their species, and here your task is to train a machine learning model that can learn from the measurements of the iris species and classify them.

TASK 1 - Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according totheir species, and here your task is to train a machine learning model that can learn from the measurements of the iris species and classify them.#oasisinfobyte

Title: Iris Flower Classification: Unveiling the Power of Machine Learning Description: Excited to present my latest project: Iris Flower Classification! 🌸🔍 Using the classic Iris dataset, I trained a machine learning model to accurately classify Iris flowers into their respective species based on sepal and petal measurements. Join me as we explore the fascinating world of introductory classification tasks! #codsoft #datascience #machinelearning #irisclassification

Unlocking the Secrets of Iris Flowers with Machine Learning! 🌿 Excited to share my journey of training a model to classify Iris flowers into their species - Setosa, Versicolor, and Virginica. Using the Iris dataset, I've delved into the world of machine learning to create a powerful classification tool. Stay tuned for insights into how sepal and petal measurements unveil the magic of nature! #MachineLearning #IrisDataset #Classification #DataScience #codsoft

Project Title: Iris Flower Classification The dataset typically used for this project is the Iris dataset, which contains measurements of these features for three species of iris flowers: Setosa, Versicolor, and Virginica. An iris flower classification project involves using machine learning algorithms to classify iris flowers into different species based on their characteristics such as sepal length, sepal width, petal length, and petal width. Project usually includes steps as data preprocessing, model training, evaluation, and deployment. Popular machine learning algorithms for this task include Decision Trees, Support Vector Machines, and Logistic Regression. I want to extend my heartfelt gratitude to Oasis Infobyte for the opportunity. #DataScience #IrisClassification #OasisInfobyte

I have successfully completed task 3 #codsoft CodSoft The Iris flower classification project is a well-known machine learning exercise that involves classifying iris flowers into three species: Iris-setosa, Iris-versicolor, and Iris-virginica

I have completed this task:- The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements. Now, imagine that you have the measurements of Iris flowers categorized by their respective species. Your objective is to train a machine learning model that can learn from these measurements and accurately classify the Iris flowers into their respective species. Use the Iris dataset to develop a model that can classify iris flowers into different species based on their sepal and petal measurements. This dataset is widely used for introductory classification tasks. CodSoft #codsoft

# Iris flower Classification 🌸 Iris classification involves distinguishing between three species of iris flowers – setosa, versicolor, and virginica🌺 – based on measurements of their sepal length and width, as well as petal length and width.💮 Machine learning algorithms, often trained on a dataset with labeled iris samples, are used to make accurate predictions 🔎💡about the species of a given iris based on its features.🔎✨💫 #oasisinfobyte #oasis #oasisfamily

Have you wondered if those insects flying around you are honey bees? Well, use the Honey Bee Society's new ML model to get a prediction! 🐝 We trained a MultiModalPredictor from the AutoGluon library to classify images of bees, focusing primarily on Honey Bees. The model is fine-tuned on 70,000 inaturalist images containing and achieved accuracy of 97.5%. The model evaluates the image and predicts whether it contains a honey bee, bumblebee, or a vespidae (wasp, hornet, etc.). Interact with it on: Streamlit: https://lnkd.in/gQS_ScPR Our website: https://lnkd.in/grXFrfBn Huggingface: https://lnkd.in/gZe_4bR3

Check out the study "Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine." This study presents a novel method for mapping the natural distribution of highland #bamboo (Oldeania alpina) using spectral bands and three machine learning algorithms: random forest, gradient tree boosting, and classification and regression tree. Download it here: https://lnkd.in/dVAJ-zhq #thinkbamboo #INBAR