A repository that outlines how to train a Random Forest regressor using Mllib Pyspark.

The basic steps followed are as below:

1. Install Spark on Google Colab and load a dataset in PySpark
2. Describe and clean your dataset
3. Create a Random Forest pipeline to predict car prices
4. Create a cross validator for hyperparameter tuning
5. Train your model and predict test set car prices
6. Evaluate your model’s performance via several metrics

The link to the Coursera Project is below: https://www.coursera.org/projects/spark-machine-learning-pipeline-python