Project: Predict Sales Revenue with scikit-learn

3.9
21件の評価
5件のレビュー
提供:
Rhyme
このガイド付きプロジェクトでは、次のことを行います。

Build simple linear regression models in Python

Apply scikit-learn and statsmodels to regression problems

Employ explorartory data analysis (EDA) with seaborn and pandas

Explain linear regression to both technical and non-technical audiences

Clock2 hours
Beginner初級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. By the end of this course, you will be able to: - Explain the core ideas of linear regression to technical and non-technical audiences - Build a simple linear regression model in Python with scikit-learn - Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas - Evaluate a simple linear regression model using appropriate metrics This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, 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 Jupyter and Python 3.7 with all the necessary libraries 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.

あなたが開発するスキル

Machine LearningData Visualization (DataViz)Linear RegressionExploratory Data AnalysisScikit-Learn

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. Introduction and Overview

  2. Loading the Data and Importing Libraries

  3. Removing the Index Column

  4. Exploratory Data Analysis (EDA)

  5. Relationship between Predictors and Response

  6. Creating the Simple Linear Regression Model

  7. Evaluation and Model Parameters

  8. Making Predictions with the Model

  9. Model Evaluation Metrics

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です。

分割画面のビデオで、講師が手順ごとにガイドします

よくある質問

よくある質問

  • By purchasing a guided project, you'll get everything you need to complete the guided project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

  • Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, guided projects are not available on your mobile device.

  • Guided project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

  • You can download and keep any of your created files from the guided project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

  • Financial aid is not available for guided projects.

  • Auditing is not available for guided projects.

  • At the top of the page, you can press on the experience level for this guided project to view any knowledge prerequisites. For every level of guided project, your instructor will walk you through step-by-step.

  • Yes, everything you need to complete your guided project will be available in a cloud desktop that is available in your browser.

  • You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.