akshaykoganur / placement_prediction

Placement prediction website(Flask web application) predicts the chance of getting placed on-campus based on various parameters like CGPA, backlogs, internships, etc. This website uses a Machine Learning model trained using Random Forest Classification technique. The machine learning model achieved 94% precision and 88% accuracy.

Placement Prediction Web Application

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Overview:

The Online Placement Prediction System, where student can estimate their chances of securing on-campus placements, considering several parameters such as Stream, CGPA, Internship, Backlogs and more. Students can estimate their results within few clicks by submitting short form. The website usage the machine learning model to analyze the input parameters. The dataset downloaded from the www.Kaggle.com for testing.

Features:

• By leveraging the Random Forest Classification technique, the model has been train to analyze the input parameteers and makes predictions ragarding the outcome.
• The Machine Learing Model (Random Forest Classification) achieved 94% precision and 88% accuracy.

Tech Stack:

• HTML, CSS, Python.
• Flask framework.
• Jupyter Notebook.

Screenshots:

Home page:

User have to fill this form.

Result Page:

User redirected to the result page after submitting form.

Website Link:

https://placement-prediction-18gm.onrender.com/

Usage:

Steps:

1. Clone Project

git clone https://github.com/akshaykoganur/placement_prediction.git

2. Installing dependancies

pip install -r requirements.txt

3. Run

python app.py

Contributors

• Akshay Koganur
• Avinash Maharnavar