Fake Profile Detection

🚨 Fake Instagram Profile Detection using Machine Learning

This project is a real-time Instagram profile analyzer that predicts whether a given profile is fake or real using machine learning. It uses profile metrics like follower count, following count, post count, and verification status to make predictions.

📌 How It Works

You enter an Instagram username.
The application uses the Apify API to fetch public profile data.
It extracts key features such as:
- Number of followers
- Number of followings
- Number of posts
- Is the account private?
- Is the account verified?
These features are passed into a pre-trained machine learning model (classifier.pkl) to predict whether the profile is real or fake.

🛠 Technologies Used

Python
Streamlit – for building the web app
Joblib – for loading the ML model
Apify API – to scrape Instagram data
Scikit-learn – for training the ML model
Pandas, NumPy – for data manipulation

🧠 ML Model

The model is trained using a labeled dataset containing Instagram profile attributes. The classification is binary:

0 → Likely Fake
1 → Likely Real

The training includes feature normalization and multiple algorithm trials like Logistic Regression, Decision Trees, and Random Forests. The final deployed model is chosen based on accuracy and generalization.

🖥️ Project UI

The app is built with Streamlit for a clean and interactive interface.
Users simply input a username and click Predict.
Output shows the profile’s stats and the prediction result with appropriate messaging (Success/Error).