【Data Science Project】 Building a Machine Learning Pipeline for a Predictive Car Price Model with PySpark

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The goal of this data science project is to develop a predictive model for estimating the Manufacturer’s Suggested Retail Price (MSRP) of cars. Accurate price predictions are crucial for both manufacturers and consumers, enabling better decision-making in terms of production, marketing, and purchasing. In this project, we leverage PySpark, a powerful data processing framework, to analyze and model the relationships between various car features and their prices.

Here is my notebook with full code.

I. Summary of Steps:

1. Environment Setup:

• Install and configure necessary tools, including Apache Spark with PySpark, to create an efficient and scalable data processing environment.
• This is to establish a robust environment for data processing and machine learning using PySpark to handle large-scale datasets efficiently.

2. Data Loading and Inspection:

• Load the car dataset into a Spark DataFrame and inspect its structure using PySpark methods (show(), printSchema(), describe()).
• Understand the data types, features, and overall characteristics of the dataset.