At a Glance

Name: Precise Predictions: Classification for Flower and Tumors
Price: Free CAD
Rating: 4.7 (260 reviews)
Author: ramesh_sannareddy, vicky_kuo

Master essential skills for mind-blowing predictions! In this project, you'll discover how to build powerful machine learning regressors to predict flower species and cancer. Gain in-demand expertise for success and unleash your potential in the world of data-driven success.

You will learn how to build a machine learning classifier from this hands-on project. By utilizing the Scikit-learn library, you will develop a tumor classification machine learning model that can effectively identify malignant and benign tumor samples.

The significance of this project lies in its ability to equip you with practical knowledge that can be applied to real-world scenarios. Through hands-on experience, you will learn the step-by-step process of building a classifier and gain insights into the intricate world of flowers and tumor classification.

A Look at the Project Ahead

Dataset Preparation:
- As a first step, you will gather datasets of iris and tumor samples.
Feature Selection:
- Identify relevant features from the dataset that are informative for types classification.
Model Training and Evaluation
- This stage involves training your selected model using the dataset and evaluating your model performance.

What You'll Need

Ignite your machine learning journey with just 30 minutes of focused dedication! This beginner-friendly project requires minimal preparation, allowing you to dive right into the captivating world of machine learning. Thanks to the IBM Skills Network Labs environment, you'll enjoy a hassle-free setup with pre-installed tools, guaranteeing a seamless experience.

Offered By: IBMSkillsNetwork

Precise Predictions: Classification for Flower and Tumors

At a Glance

A Look at the Project Ahead

What You'll Need

This Guided Project is part of the following Learning Paths