Placement Prediction
A simple project based on Placement and Salary Prediction is a professional-level web application developed using Python and Flask. The main aim of this project is to use machine learning algorithms to predict whether a student will get placed in campus recruitment and, if placed, also estimate the expected salary package.
This project is very useful for colleges, training institutes, and students because it provides a data-driven way of understanding placement chances. Teachers can analyze which students need extra support, while students can get an early idea about their opportunities and prepare better. For IT students, this project is also an excellent academic work since it demonstrates how predictive analytics and ML models can be used in real-life recruitment processes.
Project Summary Table
| Attribute | Details |
|---|---|
| Project Name | Placement and Salary Prediction Using Machine Learning |
| Language/s Used | Python, HTML, CSS |
| Type | Web Application |
| Developer | UPDATEGADH |
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Available Features
The project provides multiple practical features that make it useful both academically and professionally:
Placement Prediction – The system predicts whether a student is likely to get placed or not, based on academic performance and other attributes.
Salary Prediction – If the student is predicted as “placed,” the system further estimates the expected salary package.
Real-time Prediction – Students or teachers can input details in a web form and instantly receive prediction results.
Graphical Output – Visual feedback is given through graphs such as the confusion matrix and ROC curves to understand model performance.
Lightweight & Portable – The application can be easily deployed and run on local systems without heavy setup.
Overview
The core of this project is built on the Random Forest algorithm, which is known for its accuracy and reliability in classification and regression problems. Two separate ML models are created:
Placement Prediction Model – predicts whether a student is likely to be placed in campus drives.
Salary Prediction Model – estimates the salary for students who are predicted to be placed.
These models are integrated into a Flask-based web application. Users can enter academic attributes like CGPA, internship experience, hackathon participation, and skill sets into the web form. Based on this input, the system gives instant predictions about placement chances and salary expectations.
This makes the project both a learning tool and a real-world application for analyzing student employability.
Dataset Description
The system uses two CSV datasets for training and testing the models:
Placement_Prediction_data.csv
Salary_Prediction_data.csv
These datasets contain various student details such as:
CGPA (Cumulative Grade Point Average)
Internship experience
Participation in hackathons
Skills and extracurricular activities
Other academic records
By learning patterns from this data, the machine learning models can make accurate predictions about whether a student will be placed and what salary they might expect.
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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