Overview
Today you will consolidate everything you've learned by building a complete machine learning pipeline from data preprocessing to model training, evaluation, and deployment.
You will submit your solution to a Kaggle competition and deploy your ML app using Streamlit or any preferred platform, showcasing your skills to potential employers.
Key Concepts
- End-to-end ML pipeline development
- Data preprocessing and feature engineering
- Model training, tuning, and evaluation
- Kaggle competition submission process
- Deploying ML models with Streamlit, Hugging Face, or Render
Practice Exercise
Exercise: Final ML App Project
- Choose a Kaggle dataset or competition for your project.
- Prepare and preprocess the data thoroughly.
- Train and optimize a model using techniques learned.
- Submit your prediction file to Kaggle.
- Deploy your ML pipeline as an interactive app using Streamlit or similar.
- Document your project well on GitHub and Kaggle profiles.
Resources
Streamlit Deployment Guide
Main resource for today
Kaggle Submission Guide
How to make submissions and improve scores
Deploy ML Models with Streamlit
Tutorials and examples on deploying models
Complete Today's Task
Mark today's task as complete to track your progress and earn achievements.