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Created on Cognitive Class Labs

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DDAVINDER
Last edited Jun 1, 2019
Created on Jun 1, 2019

This example shows how to build predictive models for car pricing using the Python data science stack. The notebook loads an automobile dataset from IBM Cloud Object Storage into a pandas DataFrame, then imports pandas, NumPy, and Matplotlib for analysis. It demonstrates model development techniques for estimating vehicle prices based on features like horsepower, engine size, and fuel efficiency. The code prepares data by normalizing columns and creating binary indicators, showcasing a practical workflow for regression modeling in an educational notebook environment.

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