Data Science Challenge (Coursera)

Data Science Challenge (Coursera)

In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.

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Objectives

  • Load, clean, analyze, process, and visualize data using Python and Jupyter Notebooks
  • Produce an end-to-end machine learning prediction model using Python and Jupyter Notebooks

In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.
Important Information:
How to register?
To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register.
Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes. We’ll send you a profile link the week of the challenge.
When does the challenge start?
The coding challenge begins Friday, June 30th, at 8 AM (PST) and closes Sunday, July 2nd, at 11:59 PM (PST). If you’re registered, you’ll receive a reminder email on the challenge start date.
Please note this is a timed competition. Once the challenge is unlocked, you’ll have 72 hours to complete it. You can submit as many times as you would like within this timeframe.
What will the winners receive?
Participants will be evaluated based on their model’s prediction accuracy. The top 20% of participants will receive an achievement badge on their Coursera Skills Profile, highlighting their performance to recruiters. The top 100 performers will get complimentary access to select Data Science courses.
All participants can showcase their projects to potential employers on their Coursera Skills Profile.
Winners will be notified by email the week of July 17th.
Good luck, and have fun!

Project plan
This project requires you to independently complete the following steps:
Importing and preprocessing data
Analyze the data
Build machine learning models
Evaluate machine learning models

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