Placement Assistant

This project is a web application designed to assist students in their placement preparation. It uses a machine learning model to predict the likelihood of a student getting placed and provides a personalized roadmap to improve their chances. It also includes features for practicing interview questions and visualizing placement data.

Features

• Placement Prediction: Predicts the probability of a student's placement based on their academic and personal details.
• Personalized Roadmap: Generates a 10-week roadmap to help students improve their skills and placement chances.
• Interview Practice: Provides a set of technical and general interview questions for practice.
• Answer Evaluation: Evaluates user's answers to interview questions and provides feedback.
• Data Visualization: Presents placement data in the form of charts and graphs.

Tech Stack

Backend

• FastAPI: A modern, fast (high-performance) web framework for building APIs with Python 3.7+.
• scikit-learn: A machine learning library for Python.
• Google Gemini: A family of generative AI models developed by Google.
• Joblib: A set of tools to provide lightweight pipelining in Python.

Frontend

• React: A JavaScript library for building user interfaces.
• Vite: A build tool that aims to provide a faster and leaner development experience for modern web projects.
• Tailwind CSS: A utility-first CSS framework for rapidly building custom designs.

Project Structure

The project is divided into the following directories:

• analysis: Contains images and charts generated from the analysis of the placement data.
• backend: Contains the FastAPI application that serves the machine learning model and provides the API endpoints.
• datasets: Contains the student placement data in CSV format.
• frontend: Contains the React application that provides the user interface.
• models: Contains the pre-trained machine learning models and encoders.
• train.ipynb: A Jupyter notebook for training the machine learning model.
• test.py: A Python script for testing the backend API.

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

• Python 3.7+
• Node.js
• npm

Installation

1. Clone the repo

   git clone https://github.com/your_username/placement-assistant.git

2. Install backend dependencies

   pip install -r backend/requirements.txt

3. Install frontend dependencies

   npm install --prefix frontend

4. Set up environment variables Create a .env file in the backend directory and add your Google API key:

   GOOGLE_API_KEY=your_api_key
   

Running the Application

1. Start the backend server

   uvicorn backend.main:app --reload

2. Start the frontend development server

   npm run dev --prefix frontend

The application will be available at http://localhost:5173.