Student_Performance_Prediction_System

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Student_Performance_Prediction_System

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Here are the **README.md** file and the **Deployment Guide** for your Student Performance Prediction System.

### **1. README.md File**

Create a file named `README.md` in your project folder and paste the content below. This file serves as the documentation for your project on GitHub.

```markdown
# Student Performance Prediction System 🎓

A Machine Learning web application that predicts whether a student will pass or fail based on their academic data. This system uses **Logistic Regression** and is built with **Python**, **Flask**, and **Scikit-Learn**.

## 🚀 Features
- **Web Interface:** Simple form to input student data (Internal Grades, Study Time, Failures, Absences).
- **Real-time Prediction:** Instantly calculates the pass probability using a trained ML model.
- **Data-Driven:** Trained on the UCI Student Performance dataset.
- **Responsive Design:** Clean and minimal UI.

## 🛠️ Tech Stack
- **Frontend:** HTML, CSS
- **Backend:** Python, Flask
- **Machine Learning:** Scikit-Learn, Pandas, NumPy
- **Deployment:** Render (compatible with Gunicorn)

## 📂 Project Structure
```text
Student_Prediction_Project/
│
├── static/              # CSS/Images (Optional)
├── templates/           # HTML Files
│   ├── index.html       # Input Form
│   └── result.html      # Prediction Result Page
│
├── app.py               # Flask Main Application
├── train_model.py       # Script to train and save the ML model
├── student_data.csv     # The dataset used for training
├── model.pkl            # The saved Machine Learning model (generated)
├── requirements.txt     # List of python dependencies
└── README.md            # Project Documentation

```

## ⚙️ Local Installation & Setup

1. **Clone the Repository**
```bash
git clone [https://github.com/your-username/student-predictor.git](https://github.com/your-username/student-predictor.git)
cd student-predictor

```

2. **Install Dependencies**
```bash
pip install -r requirements.txt

```

3. **Train the Model**
Run the training script to generate the `model.pkl` file.
```bash
python train_model.py

```

4. **Run the Application**
```bash
python app.py

```

5. **Access the App**
Open your browser and go to: `http://127.0.0.1:5000/`

## 📊 Dataset Information

The model uses the following features for prediction:

* **G1:** First period grade (numeric: 0-20)
* **Study Time:** Weekly study time (1: <2 hrs, 2: 2-5 hrs, 3: 5-10 hrs, 4: >10 hrs)
* **Failures:** Number of past class failures (numeric: n if 1<=n<3, else 4)
* **Absences:** Number of school absences (numeric: 0-93)

## 📜 License

This project is open-source and available for educational purposes.

```

---

### **2. Deployment Guide (Render.com)**

Follow these steps to put your website on the internet using Render (a free cloud hosting provider).

#### **Step 1: Final Check of Your Files**
Before uploading, ensure you have the `requirements.txt` file created in your project folder. This tells the server what libraries to install.

**`requirements.txt` content:**
```text
Flask
scikit-learn
pandas
numpy
gunicorn

```

*(Note: `gunicorn` is essential for production deployment on Render.)*

#### **Step 2: Upload to GitHub**

Render pulls your code directly from GitHub, so you need to push your code there first.

1. Log in to [GitHub.com](https://github.com/) and create a **New Repository**. Name it `student-predictor`.
2. Open your terminal (or Command Prompt) in your project folder.
3. Run the following commands one by one:
```bash
git init
git add .
git commit -m "Initial commit of Student Prediction System"
git branch -M main
git remote add origin https://github.com/YOUR-USERNAME/student-predictor.git
git push -u origin main

```

*(Replace `YOUR-USERNAME` with your actual GitHub username).*

#### **Step 3: Deploy on Render**

1. Go to [Render.com](https://render.com/) and create a free account.
2. Click the **"New +"** button and select **"Web Service"**.
3. Select **"Build and deploy from a Git repository"**.
4. Find your `student-predictor` repository in the list and click **"Connect"**.
5. **Configure the Service:**
* **Name:** `student-predictor` (or any unique name).
* **Region:** Choose the region closest to you (e.g., Singapore).
* **Branch:** `main`
* **Runtime:** **Python 3**
* **Build Command:** `pip install -r requirements.txt`
* **Start Command:** `gunicorn app:app`

6. Scroll down and click **"Create Web Service"**.

#### **Step 4: Wait for Build**

Render will now clone your code, install the libraries, and start the server. This usually takes 2-3 minutes. You can watch the logs in the dashboard.

* **Success:** You will see a message saying "Your service is live."
* **Access:** Click the URL provided at the top left (e.g., `https://student-predictor.onrender.com`).

#### **Troubleshooting Common Errors**

* **"Model not found":** If the app crashes saying `model.pkl` is missing, it means you forgot to push the `model.pkl` file to GitHub. Run `python train_model.py` locally, then `git add .`, `git commit`, and `git push` again.
* **"Internal Server Error":** Check the "Logs" tab in Render to see the specific Python error message.

---

### **Next Step**

Would you like me to help you create a **presentation script** to explain this project during your final submission?

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