Basic Data Analysis and Model Building using Python

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In this Guided Project, you will:

Ingest a dataset into a Jupyter Notebook environment using Pandas and create visualizations using the Matplotlib and Seaborn libraries.

Preprocess data through standardization, resampling using Scikit-Learn.

Build, train and save an accurate binary classification model.

Clock1-1.5 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

By the end of this project learners will be able to perform data ingestion using Pandas and Numpy, preprocess and visualize the data using Matplotlib and Seaborn. Learners will also build a binary classification model using Scikit-Learn. Additionally, learners will be introduced to other data science concepts and techniques such as standardization and up- and down-sampling of biased data. It is important to manipulate your data such that it can then be used to build a predictive model. This Guided Project was created by a Coursera community member.

Skills you will develop

Data AnalysisData Visualization (DataViz)Building classification modelsData standardization

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Data ingestion and visualization

  2. Data preprocessing (standardization and scaling)

  3. Data preparation and Principal Component Analysis intro

  4. Build and train a binary classification model

  5. Versioning

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.