https://github.com/sunita10sonar/admissionpredict_ann

Artificial Neural Network regression model to predict graduate admission chances based on academic profiles.
https://github.com/sunita10sonar/admissionpredict_ann

deep-learning machine-learning neural-network regression tensorflow

Last synced: about 2 months ago
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Artificial Neural Network regression model to predict graduate admission chances based on academic profiles.

• Host: GitHub
• URL: https://github.com/sunita10sonar/admissionpredict_ann
• Owner: Sunita10Sonar
• Created: 2025-09-01T12:12:23.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-09-01T12:18:30.000Z (10 months ago)
• Last Synced: 2025-09-01T14:25:07.265Z (10 months ago)
• Topics: deep-learning, machine-learning, neural-network, regression, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 36.1 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🎓 Admission Prediction using ANN (Regression Model)

## ⭐ Situation

Graduate school admission is highly competitive, and students often want to estimate their **chance of admission** before applying.  

Traditional statistical methods can struggle to capture the nonlinear relationships between factors such as GRE, TOEFL, CGPA, university rating, SOP, LOR, and research experience.

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## 🎯 Task

The objective of this project is to **predict the probability of admission** based on a student’s profile by:  

- Preprocessing academic data.  

- Building and training an **Artificial Neural Network (ANN)** regression model.  

- Evaluating prediction accuracy with proper metrics.  

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## 🔧 Action

Steps taken to achieve the task:  

1. **Data Exploration & Preprocessing**  

   - Cleaned and normalized the dataset.  

   - Performed exploratory data analysis (EDA) to understand correlations.  

2. **Feature Engineering**  

   - Selected key features (GRE, TOEFL, CGPA, etc.).  

   - Split the dataset into training and test sets.  

3. **Model Development**  

   - Built an ANN regression model using **TensorFlow/Keras**.  

   - Tuned hyperparameters (hidden layers, activation functions, optimizer).  

4. **Model Evaluation**  

   - Assessed accuracy using **Mean Squared Error (MSE)** and **R² score**.  

   - Visualized actual vs. predicted admission probabilities.  

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## 📊 Result

- Successfully built an ANN regression model to predict **chance of admission** (0–1 scale).  

- Found that **CGPA, GRE, and Research experience** are the most influential factors.  

- Model provides a data-driven way for students to **assess admission likelihood** before applying.