Cervical Cancer Risk Prediction Using Machine Learning

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

U​nderstand the theory and intuition behind XGBoost Algorithm

P​reform exploratory data analysis

Develop, train and evaluate XG-Boost classifier model using Scikit-Learn

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

In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records.

您要培养的技能

  • Data Analysis
  • Machine Learning
  • classification
  • Artificial Intelligence(AI)

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. Task #1: Understand the Problem Statement and Business Case

  2. Task #2: Import Libraries and Datasets

  3. Task #3: Perform Exploratory Data Analysis

  4. Task #4: Perform Data Visualization

  5. Task #5: Prepare the data before Model Training

  6. Task #6: Understand the Theory and Intuition Behind XG-Boost

  7. Task #7: Train and Evaluate XG-Boost Algorithm

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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