Statistical Data Visualization with Seaborn
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.
Habilidades que desarrollarás
Aprende paso a paso
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Project Overview
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
Cómo funcionan los proyectos guiados
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
Reseñas
Principales reseñas sobre STATISTICAL DATA VISUALIZATION WITH SEABORN
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.
Preguntas Frecuentes
Preguntas Frecuentes
¿Qué obtendré si compro un proyecto guiado?
¿Hay proyectos guiados disponibles en computadoras de escritorio y dispositivos móviles?
¿Quiénes son los instructores para proyectos guiados?
¿Puedo descargar el trabajo de mi proyecto guiado después de completarlo?
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
¿Puedo participar como oyente de un proyecto guiado y mirar el video de forma gratuita?
¿Cuánta experiencia necesito para hacer este proyecto guiado?
¿Puedo completar este proyecto guiado directamente a través de mi navegador web, en lugar de instalar un software especial?
¿Cómo es la experiencia de aprendizaje con los proyectos guiados?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
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