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Assignment 2 - Decision Trees and Random Forests

Machine Learning with Python: Zero to GBMs

Deadline: August 9th, 11:59 PM GMT

In this assignment, you'll continue building on the previous assignment to predict the price of a house using information like its location, area, no. of rooms, etc. We'll follow a step-by-step process:

  1. Download and prepare the dataset for training
  2. Train, evaluate, and interpret a decision tree
  3. Train, evaluate, and interpret a random forest
  4. Tune hyperparameters to improve the model
  5. Make predictions and save the model

Assignment Notebook

Use the starter notebook(s) to get started with the assignment. Read the problem statement, follow the instructions, add your solutions, and make a submission.

Make a Submission

Use the following command to submit directly from the notebook
Notebook Link (Required)
You can submit multiple times. Only your last submission will be evaluated.

Evaluation Critria

To receive a PASS grade for this assignment, your submission should meet the following criteria:

  • The submitted link should be a Jovian notebook marked public or secret (not private)
  • All the questions in the notebook should be answered correctly.
  • The notebooks should be executed end-to-end, showing proper outputs.
  • The validation loss shouldn't be NaN, Infinity, or a very large number
  • There shouldn't be any errors or exceptions in the notebook.
  • Your notebook must not be plagiarized i.e. copied from someone else's work.

Evaluations may take up to 1-2 weeks.