🎓 Student Performance Prediction using ANN

📌 About Project

This is a simple machine learning project where I built a model to predict whether a student will pass or fail.

The prediction is based on some basic inputs like:

• Study Hours
• Attendance
• Previous Marks
• Assignments Completed
• Sleep Hours

I also created a simple web app using Streamlit where users can give input and see the prediction instantly.

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🧠 What I Used

• Python
• TensorFlow (for ANN model)
• Scikit-learn (for preprocessing)
• Streamlit (for UI)

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🔄 How This Project Works

Step 1: Data Collection

I created a dataset with student details like study hours, attendance, marks, etc.

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Step 2: Data Preprocessing

I split the data into training and testing parts.
Then I used StandardScaler to scale the data.

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Step 3: Model Building

I built an Artificial Neural Network (ANN) using TensorFlow.

• Input: 5 features
• Hidden layers: used ReLU activation
• Output: Sigmoid (gives result between 0 and 1)

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Step 4: Training

I trained the model using:

• Binary Cross Entropy loss
• Adam optimizer

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Step 5: Evaluation

After training, I tested the model and got around 90% accuracy.

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Step 6: Saving Model

I saved the model and scaler so that I can reuse them in the app.

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Step 7: Web App (Streamlit)

I created a simple UI where:

• User can adjust sliders
• Click predict button
• Get PASS / FAIL result
• See confidence score
• See explanation (why result came)

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📊 How Prediction Works

The model gives a value between 0 and 1.

• If value > 0.5 → PASS
• If value < 0.5 → FAIL

Example:

• 0.85 → PASS
• 0.20 → FAIL

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🚀 How to Run This Project

Follow these steps to run the project on your system:

1. Clone the repository

git clone https://github.com/PyarasaniUday/student-performance-ann.git
cd student-performance-ann

install required libraries
cd pip install -r requirements.txt

Run the application
cd streamlit run app.py


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