Placement Prediction Using Machine Learning

Placement Prediction Using Machine Learning

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Placement Prediction

This is a professional-level web application built using Python and Flask. It uses machine learning to predict whether a student will get placed and also estimates the expected salary based on their academic performance and activities.The project is useful for colleges, teachers, and students. It helps them understand placement chances and make better decisions. This tool is a real-world example of how predictive analytics can be used in the recruitment process.

Project Summary Table

Project Name Placement and Salary Prediction Using Machine Learning
Language/s Used Python, HTML, CSS
Type Web Application

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Available Features

  • Predict student placement status
  • Predict expected salary for placed students
  • Real-time prediction through web form
  • Visual feedback via graphs (confusion matrix, ROC, etc.)
  • Lightweight and portable

Overview

This project uses Random Forest algorithms to build two machine learning models:

  1. Placement Prediction – whether a student is likely to be placed.
  2. Salary Prediction – estimated salary for those placed.

The system is deployed through a Flask web application where users can input student attributes and get real-time predictions.

Dataset Description

Two CSV datasets are used:

  • Placement_Prediction_data.csv
  • Salary_prediction_data.csv

Each includes student information such as:

  • CGPA (Cumulative Grade Point Average)
  • Internship experience
  • Hackathon participation
  • Skills and other relevant features

Project Structure

Placement_Prediction_Using_Machine-Learning/
│
├── app.py                            # Flask application
├── Placement_Prediction.py          # Placement model training
├── Salary_prediction.py             # Salary model training
├── model.pkl                        # Trained placement model
├── model1.pkl                       # Trained salary model
├── preprocessing.ipynb              # Data preprocessing notebook
├── Placement_Prediction_data.csv    # Placement dataset
├── Salary_prediction_data.csv       # Salary dataset
├── requirements.txt                 # Python package list
│
├── static/
│   ├── css/                         # Stylesheets
│   └── images/                      # Confusion matrix, ROC, etc.
│
└── templates/                       # HTML pages for the Flask UI

Data Preprocessing

Done in preprocessing.ipynb, preprocessing includes:

  • Handling missing values
  • Encoding categorical data
  • Feature scaling
  • Feature selection

Model Training

Two separate Random Forest Classifiers are trained:

  • Placement Classifier – Predicts whether the student will be placed.
  • Salary Regressor – Predicts salary (only for placed students).

Steps:

  • Data split into training/testing sets
  • Model training
  • Hyperparameter optimization (if needed)

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