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 Fake1 → 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).
