https://github.com/arpan132002/college-admission-prediction-linearregression

This project aims to predict college admission chances based on various features using a Linear Regression model.
https://github.com/arpan132002/college-admission-prediction-linearregression

machine-learning-projects

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This project aims to predict college admission chances based on various features using a Linear Regression model.

• Host: GitHub
• URL: https://github.com/arpan132002/college-admission-prediction-linearregression
• Owner: arpan132002
• Created: 2024-08-04T12:27:09.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-05T12:10:35.000Z (5 months ago)
• Last Synced: 2024-08-05T13:58:19.096Z (5 months ago)
• Topics: machine-learning-projects
• Language: Jupyter Notebook
• Homepage:
• Size: 104 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# College Admission Prediction using Linear Regression

## Overview

This project aims to predict college admission chances based on various features using a Linear Regression model. By analyzing historical data, the model helps in understanding the factors that influence admission decisions and provides insights into the admission process.

## Features

- **Data Preprocessing:** Handling missing values, feature scaling, and encoding categorical variables.

- **Exploratory Data Analysis (EDA):** Visualizing data to uncover patterns and relationships.

- **Model Training:** Implementing and training a Linear Regression model.

- **Model Evaluation:** Assessing model performance using metrics like Mean Squared Error (MSE) and R-squared.

- **Prediction:** Predicting admission chances for new applicants.

## Dataset

The dataset used for this project contains information on applicants, including:

- GRE Score

- TOEFL Score

- University Rating

- Statement of Purpose (SOP)

- Letter of Recommendation (LOR)

- CGPA

- Research Experience

- Admission Decision (Target variable)

## Requirements

- Python 3.x

- pandas

- numpy

- scikit-learn

- matplotlib

- seaborn

## Results

- The model achieved an R-squared score of 0.82 on the test set.

- Significant features influencing admission decisions were CGPA, GRE Score, and Research Experience.