| 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. | |
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| ## 📌 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. | |
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| ## 🛠 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 | |
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| ## 🧠 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. | |
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| ## 🖥️ 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). | |
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