Flask + ML: The Portfolio Project That Gets You Hired

Link to the code files used in the video - https://drive.google.com/drive/folder...

🚀 In this video, I build a real industrial-grade Machine Learning application using:

✔ Flask backend API
✔ Clean & secure model deployment pipeline
✔ HTML + Bootstrap frontend
✔ Pickle + Scikit-Learn Pipeline
✔ End-to-end ML system: Training → API → Frontend → Prediction

This is NOT a toy Streamlit demo.
This is the kind of app companies actually expect from freshers in AI/ML roles — real backend, real API, real form inputs, and real inference.

🔥 What you will learn in this tutorial

✔ How to train an ML model using Scikit-Learn
✔ Why beginners must shift from Streamlit → Flask for industry projects
✔ How to build an API endpoint (/predict)
✔ How to create a frontend that talks to your ML backend
✔ How to pass form data to Flask
✔ How to return prediction + probability
✔ How to structure an end-to-end ML project
✔ How to deploy ML models in real workflows
✔ How to build portfolio projects that impress recruiters

💼 Why this project is important?

Because real companies expect you to know:

✔ Backend basics
✔ How ML pipelines work in production
✔ HTML forms → Flask routes → Model inference
✔ Industry-grade ML apps (NOT Streamlit demos)

By the end, you will be able to build actual ML apps that can be deployed on AWS, Render, Railway, EC2, or any cloud platform.

🧠 Topics Covered

Machine Learning Model Training

Model Saving with Pipeline

Flask API development

HTML form handling

Frontend + Backend integration

Production-ready ML inference

Debugging common deployment issues

Link to video for Easy Deployment tools -    • ML Deployment Made Easy 🚀 | 5 Tools Every ...  

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