# student marks predictor– FastAPI + ML + Docker

Welcome to Student Marks Predictor!
This isn't just another Python app — it's a smart prediction engine 🚀 built with FastAPI and powered by multiple machine learning models. You give it a few student details… and 💥 out comes a mark prediction!

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🧠 What’s This All About?

This project:

• Takes in student info like test scores, lunch type, and parental education 🎒
• Tries multiple ML models (Linear Regression, Random Forest, etc.) and picks the best 🧠
• Serves predictions using a fast and modern FastAPI backend ⚡
• Is fully Dockerized for easy setup anywhere 🐳

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🗂️ Project Structure

student-marks-prediction/ ├── app/ ← FastAPI app & ML logic │ ├── main.py ← Launches the app │ ├── components/ ← Ingestion, preprocessing, model training │ ├── utils.py ← Reusable helpers ├── artifacts/ ← Trained model & preprocessor (auto-generated) ├── Dockerfile ← For containerizing everything ├── requirements.txt ← All dependencies └── README.md ← You are here!

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🚀 Run It in 1-2-3

1. Clone this repo:

git clone https://github.com/your-username/student-marks-prediction.git
cd student-marks-prediction
Build the Docker image:
docker build -t student-predictor .
Run the container:
docker run -p 8080:8080 student-predictor
Open the Swagger docs:
Visit
http://localhost:8080/docs
🎯 and test the API with your inputs!

🔍 How It Works

Student data is submitted to /predictdata
The app loads:
A preprocessor (for encoding, scaling, etc.)
The best ML model chosen after comparing several during training
Prediction is made and returned like:
{
  "predicted_marks": 83.2
}
🔬 Models Tried

During training, these models were compared:

Linear Regression
Decision Tree
Random Forest
Gradient Boosting
XGBoost
CatBoost
✅ The best one based on R² score is saved and used for predictions.

🧰 Tech Stack

Tool	Purpose
FastAPI	High-speed modern API backend ⚡
Scikit-learn	Model training & preprocessing 🔧
Pandas	Data wrangling 📊
Docker	Containerization 🐳
Matplotlib/Seaborn	Exploratory visuals 🧠
📦 For Manual Setup (without Docker)

pip install -r requirements.txt
uvicorn app.main:app --reload
🧪 Sample Input

{
  "gender": "female",
  "race_ethnicity": "group C",
  "parental_level_of_education": "associate's degree",
  "lunch": "free/reduced",
  "test_preparation_course": "none",
  "reading_score": 68,
  "writing_score": 70
}