Course Project - Real-World Machine Learning Model

Machine Learning with Python: Zero to GBMs

In the course project, you will apply the machine learning skills covered in this course by training an ML model on a real-world dataset. Follow these steps to complete your project:

  1. Pick a large real-world dataset from Kaggle (see the "Resources" section below) and download it using opendatasets. Your training set should contain at least 50,000 rows and 5 columns of data.

  2. Read the dataset description, understand the problem statement and describe the modeling objective clearly. You can also browse through existing notebooks created by others for inspiration.

  3. Perform exploratory data analysis, gather insights about the data, perform feature engineering, create a training-validation split, and prepare the data for modeling.

  4. Train & evaluate different machine learning models, tune hyperparameters and reduce overfitting to improve the model.

  5. Report the final performance of your best model(s), show sample predictions, and save model weights. Summarize your work, share links to references, and suggest ideas for future work.

  6. Publish your Jupyter notebook to Jovian, make a submission below and share your project with the community. Optionally, you may also write a blog post and contribute to the Jovian official blog.

There is no starter notebook for the course project. Please use the "New" button on Jovian to create a new notebook, "Run on Colab" to execute it, and jovian.commit to record versions. Please review the "Evaluation Criteria" and "Resources" sections below carefully before starting your project.