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 Guided Project on Statistical Data Visualization with Seaborn, From UST. For more than 20 years, UST has worked side by side with the world’s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by their purpose, they partner with clients from design to operation. With this Guided Project from UST, you can quickly build in-demand job skills and expand your career opportunities in the Data Science field. 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 as well as 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. Using the exploratory data analysis (EDA) results from the Breast Cancer Diagnosis – Exploratory Data Analysis Guided Project, you will practice dropping correlated features, implement feature selection and utilize several 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. By the end of this Guided Project, you should feel more confident about working with data, creating visualizations for data analysis, and have practiced several methods which apply to a Data Scientist’s role. Let's get started!
Some experience in the basic programming commands of Python and a general understanding of machine learning.
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!
Un excelente curso para profundizar en habilidades prácticas tanto en temas de seaborn como en sklearn
The course was really nice however, I faced little issues while connecting to the rhyme desktop.
我能直接通過 Web 瀏覽器來完成此指導項目，而不必安裝特殊軟件嗎？