{"id":30791834,"url":"https://github.com/lovnishverma/placement-prediction","last_synced_at":"2025-10-09T04:08:39.731Z","repository":{"id":298150635,"uuid":"709503611","full_name":"lovnishverma/placement-prediction","owner":"lovnishverma","description":"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. 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The application uses a Decision Tree Classifier trained on a dataset's features to make predictions.\n\n![image](https://github.com/user-attachments/assets/6eafd86a-2e85-472a-95af-8cc69706939c)\n\n\n## Features\n- **Input Form**: Allows users to enter student details, including age, gender, stream, internship count, hostel status, CGPA, and backlogs, through a web interface.\n- **Prediction**: Predicts placement status (e.g., \"Placed\" or \"Not Placed\") using a trained Decision Tree Classifier.\n- **Web Interface**: Built with Flask and rendered using an HTML template (`placement.html`).\n- **Dataset**: Uses a local dataset (`studentPlacement.csv`) for training the model.\n\n\n## Requirements\nTo run this project, you need the following Python packages:\n- Flask\n- NumPy\n- Pandas\n- Scikit-learn\n\nYou can install the dependencies using:\n```bash\npip install flask pandas numpy scikit-learn\n```\n\n## Project Structure\n- **app.py**: The main Flask application that contains the logic for loading the dataset, training the model, and handling web requests.\n- **templates/placement.html**: The HTML template used for the web interface (not included in this file but required for rendering).\n- **studentPlacement.csv**: The dataset file containing student placement data (must be present in the project directory).\n- **README.md**: This file, providing an overview and instructions for the project.\n\n## How It Works\n1. The application loads the student placement dataset from a local CSV file (`studentPlacement.csv`).\n2. A Decision Tree Classifier is trained on the dataset's features (age, gender, stream, internship, hostel, CGPA, backlog) to predict placement status.\n3. Users access the web interface at the root URL (`/`), where they can input student details.\n4. Upon form submission, the app processes the input, makes a prediction, and displays the predicted placement status on the same page.\n\n## Usage\n1. **Clone the Repository**:\n   ```bash\n   git clone https://github.com/lovnishverma/placement-prediction.git\n   cd placement-prediction\n   ```\n\n2. **Prepare the Dataset**:\n   Ensure the `studentPlacement.csv` file is placed in the project directory.\n\n3. **Install Dependencies**:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Run the Application**:\n   ```bash\n   python app.py\n   ```\n\n5. **Access the Web App**:\n   Open a browser and navigate to `http://127.0.0.1:5000`.\n\n6. **Input Student Details**:\n   Enter the required details (age, gender, stream, internship, hostel, CGPA, backlog) in the form, then submit to see the predicted placement status.\n\n## Notes\n- Ensure the `placement.html` template is present in the `templates` folder, as it is required for rendering the web interface.\n- The dataset (`studentPlacement.csv`) must be available in the project directory, as the app reads it locally.\n- 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.\n- 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).\n\n## License\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flovnishverma%2Fplacement-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flovnishverma%2Fplacement-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flovnishverma%2Fplacement-prediction/lists"}