Breast Cancer Prediction Using Machine Learning

提供方
Coursera Project Network
在此指导 项目中,您将:

Learn to Build Logistic Regression Classifier to Classify Cancer as Malignant or Benign

Learn to download dataset directly from Kaggle using Kaggle API

Learn to work with Google Colab in Cloud

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 2 hours long project-based course, you will learn to build a Logistic regression model using Scikit-learn to classify breast cancer as either Malignant or Benign. We will use the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle. Our goal is to use a simple logistic regression classifier for cancer classification. We will be carrying out the entire project on the Google Colab environment. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in real-life. We are only using this data for educational purposes. By the end of this project, you will be able to build the logistic regression classifier to classify between cancerous and noncancerous patients. You will also be able to set up and work with the Google colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of the Logistic Regression algorithm. You will need a free Gmail account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

Python ProgrammingCancer predictionMachine LearningData Mining

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction and Import Libraries

  2. Download dataset directly from Kaggle

  3. Load & Explore the Dataset

  4. Perform LabelEncoding

  5. Split the data into Independent and Dependent sets and perform Feature Scaling

  6. Building Logistic Regression Classifier

  7. Evaluate the performance of the model

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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