Placement-Prognosticator is a Flask web application designed to predict whether a student will be placed based on their CGPA, IQ, and Profile Score. The prediction model utilizes machine learning algorithms, specifically a Support Vector Classifier (SVC) and a Random Forest Classifier.
The Flask web application (app.py) serves as the primary entry point. Users can interact with the application through a web browser, providing input for CGPA, IQ, and Profile Score to receive predictions regarding a student's placement status.
To run the web application, follow these steps:
-
Ensure you have the required Python packages installed. Install them using the following command:
pip install -r requirements.txt -
Run the Flask application by executing the following command in your terminal:
python app.py -
Open your web browser and navigate to http://127.0.0.1:5000/ to access the web application.
The machine learning model is trained using the placement_prognosticator.ipynb Jupyter Notebook. The notebook covers the following steps:
- Data loading and exploration.
- Splitting the data into training and testing sets.
- Training machine learning models (Logistic Regression, Random Forest, Support Vector Classifier).
- Saving the trained model (model.pkl) using pickle.
The required Python packages for running the web application and training the machine learning model are listed in the requirements.txt file. Install these dependencies using the command mentioned above.