---
title: GS Admission Prediction ML Approach
emoji: 📊
colorFrom: green
colorTo: red
sdk: streamlit
sdk_version: 1.42.2
app_file: app.py
pinned: false
license: mit
short_description: Graduate School Chance of Admission Prediction
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# University Graduate Admission Regression Analysis

This Streamlit app is designed to analyze admission data and perform regression analysis to predict the chance of admission based on various factors.

## About this App

This app uses a dataset containing information about applicants' GRE scores, TOEFL scores, university ratings, SOP (Statement of Purpose) scores, LOR (Letter of Recommendation) scores, CGPA (Cumulative Grade Point Average), research experience, and the chance of admission.

## Dataset Description

The dataset consists of the following columns:
- **GRE Score**: Graduate Record Examination score (out of 340)
- **TOEFL Score**: Test of English as a Foreign Language score (out of 120)
- **University Rating**: Rating of the university (out of 5)
- **SOP**: Strength of the Statement of Purpose (out of 5)
- **LOR**: Strength of the Letter of Recommendation (out of 5)
- **CGPA**: Cumulative Grade Point Average (out of 10)
- **Research**: Research experience (0 or 1)
- **Chance of Admit**: Probability of admission (0 to 1)

## Regression Analysis

The app performs regression analysis using various models to predict the chance of admission based on the input features. The models used in this app include:
- **Linear Regression**
- **Support Vector Machine (SVM)**
- **Decision Tree**
- **Random Forest**
- **K-Nearest Neighbors (KNN)**
- **MLP Regressor**

Each model's performance is evaluated using metrics such as Mean Squared Error (MSE) and R-squared (R²). The app also provides visualizations to compare the actual vs. predicted values for each model.

## Features

- **Data Preprocessing**: Handles null values, ensures consistent data types, and scales the data.
- **Feature Importance**: Uses Random Forest to determine the importance of each feature.
- **Model Training and Evaluation**: Trains multiple regression models and evaluates their performance.
- **Prediction**: Allows users to input their own data to predict the chance of admission using the selected model.

## How to Use

1. **Load the Dataset**: The app loads the dataset and displays it.
2. **Check for Null Values**: The app checks for and handles any null values.
3. **Feature Scaling**: The app scales the features using StandardScaler.
4. **Feature Importance**: The app displays the importance of each feature using a bar plot.
5. **Model Training and Evaluation**: The app trains various regression models and evaluates their performance.
6. **Prediction**: Users can input their own data to predict the chance of admission.

## Installation

To run this app locally, follow these steps:

1. Clone the repository:
   ```bash
   git clone <repository_url>