Welcome to the College Student Placement Prediction WebApp! This project leverages machine learning to predict the placement outcomes of college students. The web application is built using Flask for the backend and various other technologies for data processing and visualization.
This web application uses machine learning algorithms to predict whether a student will be placed or not based on their academic and extracurricular performance. It provides insights and visualizations to help understand the factors affecting placement.
- Predict placement status based on student data
- Visualize data distributions and relationships
- Interactive and user-friendly web interface
To get started with the project, follow these steps:
-
Clone the repository:
git clone https://github.com/chaudharijay/Campus-Placement-Predictor-Webapp-Using-ML.git cd college-placement-prediction -
Make sure you have Anaconda Navigator installed. Create a new environment using the provided environment.yml file:
conda env create -f environment.yml conda activate college-placement-prediction
- Start Jupyter Notebook:
Launch Jupyter Notebook to explore the data and models:
jupyter notebook- Run the Flask app:
Start the Flask web application:
python app.py- Open the app:
Open your web browser and go to http://127.0.0.1:5000 to interact with the web application.
- Programming Languages: Python, JavaScript
- Web Framework: Flask
- Data Processing and Analysis: NumPy, pandas, scikit-learn
- Data Visualization: Matplotlib, seaborn
- Frontend: HTML, CSS
- IDE and Tools: Anaconda Navigator, Jupyter Notebook, VS Code
We welcome contributions from the community. If you would like to contribute, please follow these steps:
1.Fork the repository
2.Create a new branch (git checkout -b feature-branch)
3.Commit your changes (git commit -m 'Add some feature')
4.Push to the branch (git push origin feature-branch)
5.Open a Pull Request