| Fake Profile Detection |
| # 🚨 Fake Instagram Profile Detection using Machine Learning |
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| 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 |
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| - 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 |
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| - **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 |
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| The model is trained using a labeled dataset containing Instagram profile attributes. The classification is binary: |
| - `0` → Likely Fake |
| - `1` → Likely Real |
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| 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 |
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| - 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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