Statistical Data Visualization with Seaborn From UST

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В этом Бесплатный проект с консультациями вы:

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

Продемонстрируйте этот практический опыт на собеседовании

Clock1.5 hours
IntermediateУчащийся среднего уровня
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopТолько для ПК

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!

Требования

Some experience in the basic programming commands of Python and a general understanding of machine learning.

Навыки, которые вы получите

  • Data Science
  • Machine Learning
  • Python Programming
  • Seaborn
  • Data Visualization (DataViz)

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  1. Project Overview

  2. Importing Libraries and Data

  3. Dropping Correlated Columns from Feature List

  4. Classification using XGBoost (minimal feature selection)

  5. Univariate Feature Selection

  6. Recursive Feature Elimination with Cross-Validation

  7. Plot CV Scores vs Number of Features Selected

  8. Feature Extraction using Principal Component Analysis

Как устроены проекты с консультациями

Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.

На разделенном экране видео преподаватель предоставляет пошаговые

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