https://github.com/lovnishverma/placement-prediction
This is a Flask-based web application that predicts whether a student is likely to be placed in a job based on input features such as age, gender, academic stream, internship experience, hostel status, CGPA, and backlog history. The application uses a Decision Tree Classifier trained on a dataset to make predictions.
https://github.com/lovnishverma/placement-prediction
flask machine-learning
Last synced: 9 months ago
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This is a Flask-based web application that predicts whether a student is likely to be placed in a job based on input features such as age, gender, academic stream, internship experience, hostel status, CGPA, and backlog history. The application uses a Decision Tree Classifier trained on a dataset to make predictions.
- Host: GitHub
- URL: https://github.com/lovnishverma/placement-prediction
- Owner: lovnishverma
- License: mit
- Created: 2023-10-24T20:20:05.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2025-07-11T14:14:27.000Z (12 months ago)
- Last Synced: 2025-08-30T15:59:34.447Z (10 months ago)
- Topics: flask, machine-learning
- Language: HTML
- Homepage: https://placement-prediction-984q.onrender.com/
- Size: 67.4 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Student Placement Prediction Web App
## Overview
This is a Flask-based web application that predicts whether a student is likely to be placed in a job based on input features such as age, gender, academic stream, internship experience, hostel status, CGPA, and backlog history. The application uses a Decision Tree Classifier trained on a dataset's features to make predictions.
## Features
- **Input Form**: Allows users to enter student details, including age, gender, stream, internship count, hostel status, CGPA, and backlogs, through a web interface.
- **Prediction**: Predicts placement status (e.g., "Placed" or "Not Placed") using a trained Decision Tree Classifier.
- **Web Interface**: Built with Flask and rendered using an HTML template (`placement.html`).
- **Dataset**: Uses a local dataset (`studentPlacement.csv`) for training the model.
## Requirements
To run this project, you need the following Python packages:
- Flask
- NumPy
- Pandas
- Scikit-learn
You can install the dependencies using:
```bash
pip install flask pandas numpy scikit-learn
```
## Project Structure
- **app.py**: The main Flask application that contains the logic for loading the dataset, training the model, and handling web requests.
- **templates/placement.html**: The HTML template used for the web interface (not included in this file but required for rendering).
- **studentPlacement.csv**: The dataset file containing student placement data (must be present in the project directory).
- **README.md**: This file, providing an overview and instructions for the project.
## How It Works
1. The application loads the student placement dataset from a local CSV file (`studentPlacement.csv`).
2. A Decision Tree Classifier is trained on the dataset's features (age, gender, stream, internship, hostel, CGPA, backlog) to predict placement status.
3. Users access the web interface at the root URL (`/`), where they can input student details.
4. Upon form submission, the app processes the input, makes a prediction, and displays the predicted placement status on the same page.
## Usage
1. **Clone the Repository**:
```bash
git clone https://github.com/lovnishverma/placement-prediction.git
cd placement-prediction
```
2. **Prepare the Dataset**:
Ensure the `studentPlacement.csv` file is placed in the project directory.
3. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run the Application**:
```bash
python app.py
```
5. **Access the Web App**:
Open a browser and navigate to `http://127.0.0.1:5000`.
6. **Input Student Details**:
Enter the required details (age, gender, stream, internship, hostel, CGPA, backlog) in the form, then submit to see the predicted placement status.
## Notes
- Ensure the `placement.html` template is present in the `templates` folder, as it is required for rendering the web interface.
- The dataset (`studentPlacement.csv`) must be available in the project directory, as the app reads it locally.
- The `eval` function is used to parse some form inputs, which is not recommended for production due to security risks. Consider using safer alternatives like `float` or `int` for parsing in a production environment.
- The dataset's structure is assumed to have features in the first columns and the target variable (placement status) in the last column, with the second-to-last column excluded (as per the code).
## License
This project is licensed under the MIT License.