🚀 Build and Deploy a Fake News Detector with Flask for Free!

Tags: #python #flask #machinelearning #webdev #project

“In a world full of noise, let’s teach machines to detect the lies.”

Hey folks! 👋
In this post, I’ll walk you through how I built and deployed a Fake News Detector using Python, Flask, and Machine Learning — and hosted it online for free. Whether you're a student, a beginner developer, or just someone who loves real news 😄 — this one’s for you!

🧠 What I Built

I created a web app that allows users to paste a news article, and it predicts whether the article is real or fake using a trained machine learning model. Simple UI, clean prediction logic, and completely self-contained.

🔧 Tech Stack

Python 3
Flask (for the web app)
Scikit-learn (for ML model)
NLTK (for text preprocessing)
HTML/CSS (for frontend)
Render.com (for free hosting)

📁 Project Structure

project/
│
├── app.py                  # Flask app
├── train_model.py          # ML training script
├── requirements.txt
├── /data                   # True.csv, Fake.csv
├── /models                 # Saved .pkl model + vectorizer
├── /templates/index.html   # UI
├── /static/style.css       # Basic CSS
├── /utils/text_cleaner.py  # Text preprocessing
└── README.md

🧪 How It Works

1. Model Training

In train_model.py:

I combined real and fake news datasets
Preprocessed the text (lowercase, remove stopwords, lemmatize)
Used TF-IDF vectorization
Trained a Logistic Regression model
Saved the model and vectorizer with pickle

2. The Flask Web App

In app.py:

Loads the model and vectorizer
Takes user input from a text box
Preprocesses the input using the same clean_text() function
Predicts using the trained model
Displays: ✅ Real News or ❌ Fake News

🌐 Hosting It for Free with Render

Step-by-Step:

Pushed code to GitHub
Signed in at Render.com
Chose New Web Service > Linked GitHub repo
Set:

Runtime: Python
Start Command: gunicorn app:app
1. Done! Got a live public URL

👉 Example: https://fake-news-detector.onrender.com

📦 Dependencies (`requirements.txt`)

flask
scikit-learn
nltk
pandas
gunicorn

🙌 Final Thoughts

I learned a lot through this project — from building a clean Flask UI, to saving/loading ML models, to deploying with ease.

If you're looking for a project that combines web development + machine learning and is hosted for free, this is perfect. 🔥

💬 Let’s Connect

Have questions or suggestions?
Drop a comment or reach out to me on GitHub / X — I’d love to see your version of this project!

⭐ Bonus: Want the Code?

Check it out here:
👉 GitHub Repo

info_brust @nihal473