Produce and customize various chart types with Seaborn
Apply feature selection and feature extraction methods with scikit-learn
Build a boosted decision tree classifier with XGBoost
Welcome to this project-based course on Statistical Data Visualization with Seaborn. Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results. In this project, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) data set. We will use the results from our exploratory data analysis (EDA) in the previous project, Breast Cancer Diagnosis – Exploratory Data Analysis to: drop correlated features, implement feature selection and feature extraction methods including feature selection with correlation, univariate feature selection, recursive feature elimination, principal component analysis (PCA) and tree based feature selection methods. Lastly, we will build a boosted decision tree classifier with XGBoost to classify tumors as either malignant or benign. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.
Importing Libraries and Data
Dropping Correlated Columns from Feature List
Classification using XGBoost (minimal feature selection)
Univariate Feature Selection
Recursive Feature Elimination with Cross-Validation
Plot CV Scores vs Number of Features Selected
Feature Extraction using Principal Component Analysis
A machine learning perspective on seaborn capacity, dealing with plots of common results when removing features or selecting important features from dataset
Great course for a beginner to be equipped with data science tools and feature selection methods for machine learning!
The course was really nice however, I faced little issues while connecting to the rhyme desktop.
The course is really good but i feel it would be even more good if there was more explanation.
购买指导项目后，您将获得完成指导项目所需的一切，包括通过 Web 浏览器访问云桌面工作空间，工作空间中包含您需要了解的文件和软件，以及特定领域的专家提供的分步视频说明。
我能直接通过 Web 浏览器来完成此指导项目，而不必安装特殊软件吗？