Implementing all ML models and feature selection techniques that can be used.

to predict flowers based on their specific features.

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.

🌼 Classify the different species of the Iris flower.

IRIS Dataset Classification

This repo contains machine learning projects for beginners.

This repository comprises three distinct machine learning projects :Titanic Survival Prediction, Movie Rating Prediction, Iris Flower Classification,

Iris Flower Classification using Python

Iris flower classification using KNN and Random forest algorithm

Data Science Internship at CodSoft

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.

Data Science Intern @letsgrowmore Foundation LGMVIP October-21

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

Learning with sklearn diabetes and iris flower dataset, single and multiple linear regression, classification with multi-layer perceptron, kneighbors and support vector machines.

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.

Projects on Data Science Internship

This repository contains the tasks for data science internship at codsoft

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.

I recently completed my five distinct tasks, as part of my data science and machine learning internship at Oasis infobyte.