Predict Career Longevity for NBA Rookies using Scikit-learn

Visualize data for insights
Create binary classification model using logistic regression
Visualize data for insights
Create binary classification model using logistic regression
By the end of this project, you will be able to apply data analysis to predict career longevity for NBA Rookie using python. Determining whether a player’s career will flourish or not became a science based on the player’s stats. Throughout the project, you will be able to analyze players’ stats and build your own binary classification model using Scikit-learn to predict if the NBA rookie will last for 5 years in the league if provided with some stats such as Games played, assists, steals and turnovers …. etc. Note: 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.
Data Science
Python Programming
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load the dataset that we will work on
Find insights in our data
Do features selection using correlation heat map
Do binary classification using logistic regression
Adjust the discrimination threshold to increase or decrease the sensitivity to false positives or to other application factors
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
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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.
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