This document summarizes a student project to classify iris flowers into three species (setosa, versicolor, virginica) using machine learning algorithms. The project aims to train models using measurements of iris flowers to predict their species. Logistic regression, k-nearest neighbors, and decision tree algorithms are explored. Python libraries like NumPy, SciPy, Jupyter Notebook, and Enthought Canopy are used for the machine learning modeling and analysis. References on previous iris flower classification projects using machine learning are also provided.

1. Flowers
Classification

2. Presented By
PRN NO. Roll No. Group Members:-
201051011 15 Anurag Rajiv Dhamne
201051013 16 Aaditya Damaji Dhotre
201051019 20 Suyash Satish Ghatkar
201051012 12 Aditya Balu Dawkhar

3. Introduction
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. The aim of the iris flower classification is to predict flowers based
on their specific features.

4. Objective
The aim of the project is to design and implement a system of pattern
recognition for the Iris flower based on Machine Learning.
This project shows the workflow of pattern recognition and how to
use machine learning approach to achieve this goal

5. Algorithm
Logistic Regression Algorithm:- LRA is a data analysis technique that uses
mathematics to find the relationships between two data factors
K-Nearest Neighbours:-The k-nearest neighbors algorithm, also known as
KNN or k-NN, is a non-parametric, supervised learning classifier, which uses
proximity to make classifications or predictions about the grouping of an
individual data point.
Decision Tree:-A decision tree is a type of supervised machine learning used
to categorize or make predictions based on how a previous set of questions
were answered

6. Technology to be used
Python was the major technology used
Following are prominent libraries/tools we used in our project.
NumPY-NumPy is a general-purpose array-processing package[1]. it provides a high-performance
multidimensional array object and tools for working with these arrays
SCIPY-SciPy is a free and open-source Python library used for scientific computing and technical
computing
JUPYTER NOTEBOOK-The Jupyter Notebook is an open-source web application that allows you
to create and share documents that contain live code, equations, visualizations, and narrative text.
ENTHOUGHT CANAOPY-Enthought Canopy is a Python for scientific and analytic computing
distribution and analysis environment, this package manager uses jupyter notebook as a
presentation layer

7. References
Shashidar T Halakatti, Shambulinga T Halakatti, "Identification Of Iris Flower
Species Using Machine Learning", IIJCS Aug 2017.
Patric Granhom, " A Study Of Pattern Recognition Of Iris Flower Based On
Machine Learning ", TURKU UNIVERSITY OF APPLIED SCIENCES, 22 Aug
2013.
Poojitha V, Shilpi Jain, Madhulika Bhadauria, Anchal Garg, " A Collection Of
IRIS Flower Using Neural Network Clustering Tool In MATLAB", IEEE 2016.

8. Thank You