Chevron Left
返回到 Statistical Data Visualization with Seaborn

學生對 Coursera Project Network 提供的 Statistical Data Visualization with Seaborn 的評價和反饋

151 個評分
31 條評論


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....



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!


26 - Statistical Data Visualization with Seaborn 的 31 個評論(共 31 個)

創建者 Zahrotul N I


Thank you for the lesson but I hope it can be much longer for the explanation.

創建者 Juste N


Great project, would have been better with a larger dataset in my opinion.

創建者 Pavithra K


project was good but i suggest u to have basic sklearn, ml practice .

創建者 Dishant T


With more explanation, this could have been better by miles.

創建者 Long N


This course was not designed well

創建者 aithagoni m