Machine Learning Engineer Project:

Model Selection, Evaluation, and Error Analysis

📌 Project Overview

Welcome to the Machine Learning Engineering challenge for Sustainity! 🚀

Your task is to develop a machine learning model of your choice, work with a provided dataset, evaluate its performance using core metrics, and analyze how to improve accuracy. Please choose only one example to work on. The goal is to assess how you define and approach a data problem rather than working on a project.

This project will assess your ability to:

✅ Choose an appropriate ML model for the given dataset.

✅ Preprocess and analyze data before training.

✅ Train and evaluate the model using appropriate metrics.

✅ Analyze model errors and propose improvements.

🛠️ Instructions

1. Download the Dataset

  • You can download the dataset here (provide a real link).

  • The dataset consists of [describe dataset briefly, e.g., sustainability-related metrics, supply chain emissions, or product attributes].

⏳ Time Estimate

  • This project is designed to be completed in 3-4 hours.

  • If you’re unable to complete all features, focus on core model implementation and error analysis.

📥 Submission Guidelines

  • Push your code to GitHub (public repo) or send a ZIP file.

  • Include a README.md with:

  • Your model choice and reasoning.

  • Steps on how to run your code.

  • Your error analysis & proposed improvements.

    Bonus (Model Comparison, Visualizationng ✅Evaluation & Metric Interpretation ✅Error Analysis & Proposed Improvements ✅)

    Bonus (Model Comparison, Visualization, Automation) 🔥

2. Choose a Machine Learning Model

  • You are free to choose any ML model that you are comfortable with (e.g., Linear Regression, Random Forest, XGBoost, CNN, Transformer-based models).

  • Explain why you chose this model and how it fits the dataset.

5. Data Validation & Error Handling

  • Handle missing fields, incorrect data types, and invalid mappings gracefully.

  • Log errors properly using Winston or Bunyan.

3. Train & Evaluate the Model

  • Work with the given dataset to preprocess, clean, and format the data for your chosen model.

  • Train the model and evaluate it using appropriate core metrics (e.g., RMSE, Accuracy, F1-score, etc.).

4. Error Analysis & Model Improvement

  • Highlight where the model performs well and where it struggles (e.g., certain categories, missing data, biases).

  • Propose and implement at least one method to improve the model’s performance (e.g., feature engineering, hyperparameter tuning, different model architecture).

⭐ Bonus Tasks (Optional, if Time Permits)

✅ Model Comparison – Train two different models and compare their performances.

✅ Visualization – Provide meaningful plots (e.g., confusion matrix, feature importance, error distribution).

✅ Automated Training Pipeline – Use MLflow or Prefect to automate preprocessing, training, and evaluation.