Project Title

Predicting Student Placement: CGPA and Resume Score

Overview

This project aims to create a machine learning model that can predict whether a student will be placed or not based on their CGPA and resume score.

Table of Contents

• Project Description
• Technologies Used
• Dataset
• Installation Guide
• Usage

Project Description

Predicting student placement is a crucial step for universities and recruiters in understanding and enhancing the employability of students. This machine learning model uses CGPA and resume scores as input features to predict the likelihood of a student being placed.

Features

• Input Features: CGPA, Resume Score
• Output: Placement status (Placed/Not Placed)

Technologies Used

• Python
• Scikit-learn
• Pandas
• Numpy
• Matplotlib
• Seaborn

Dataset

The dataset contains information on students' CGPA and resume scores along with their placement status. It is sourced from university placement data and preprocessed for the model's use.

Dataset Structure

• CGPA: Cumulative Grade Point Average of the student
• Resume Score: A score calculated based on resume attributes such as skills, projects, etc.
• Placement Status: Binary target variable (Placed/Not Placed)

These models are trained to predict the placement status based on CGPA and resume score.

Installation Guide

1. Clone the repository:

   git clone https://github.com/YourUsername/StudentPlacementPrediction.git
   

2. Navigate to the project directory:

   cd StudentPlacementPrediction
   

3. Install required dependencies:

   pip install -r requirements.txt
   

Usage

1. Prepare your data with CGPA and resume score.

2. Load the trained model:

   import pickle
    import numpy as np
    pipe = pickle.load(open('pipe.pkl','rb'))
   

3. Use the model to predict placement:

   input_data = [[cgpa, resume_score]] # Replace with your data prediction = model.predict(input_data) print(prediction)