saurabhsinghdhami / placement-prognosticator

This app utilizes machine learning to predict student placement outcomes based on CGPA, IQ, and Profile Score, aiding both students and institutions in crucial placement decisions.

Placement-Prognosticator 🚀

Overview 📚

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.

Web Application 🌐

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.

Instructions to Run the Web Application ⚙️

To run the web application, follow these steps:

1. Ensure you have the required Python packages installed. Install them using the following command:

   pip install -r requirements.txt
   

2. Run the Flask application by executing the following command in your terminal:

   python app.py
   

3. Open your web browser and navigate to http://127.0.0.1:5000/ to access the web application.

Machine Learning Model 🤖

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.

Project Dependencies 🛠️

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.