Iris Flower Classification Using ML

Welcome to our YouTube channel!

This is my Internship project from ‪@oasisinfobyte‬

In this exciting video, we delve into the world of machine learning and explore the fascinating task of classifying Iris flowers based on their measurements. Iris flowers come in three species: Setosa, Versicolor, and Virginica, each with distinct measurement characteristics. Our goal is to train a machine learning model using popular libraries like NumPy, Pandas, Seaborn, and Matplotlib, and then put it to the test with new, unseen data.

First, we introduce the Iris flower dataset, a widely used dataset in the field of machine learning. This dataset contains measurements of sepal length, sepal width, petal length, and petal width for 150 Iris flowers, with 50 samples for each species. We explore the dataset, visualize it using Seaborn and Matplotlib, and gain insights into the different species based on their measurements.

Next, we dive into the code and demonstrate how we utilize the power of NumPy and Pandas to preprocess and manipulate the data. We split the dataset into training and testing sets, ensuring the model is evaluated on unseen data.

With the data prepared, we introduce three machine learning algorithms: Support Vector Machine (SVM), Logistic Regression, and Decision Tree Classifier. We explain the concepts behind these algorithms and demonstrate how to train them using the Iris flower dataset. Each model learns from the measurements of the Iris species, extracting patterns and relationships to make accurate predictions.

After training the models, we present the new test data, where the species of the Iris flowers are unknown. We input the measurements into the trained models and witness their classification accuracy. The results are truly remarkable as the models successfully guess the species of the Iris flowers with high accuracy.

My GitHub profile :
https://github.com/data-enthusiast-sh...

This project Github Repository -:
https://github.com/data-enthusiast-sh...

My LinkedIn -
  / shubham-oli-12911so  

Thank you for watching, and I hope this project provides valuable insights into the world of machine learning and bike demand prediction.

Don't forget to like, comment, and subscribe for more exciting content!