https://github.com/rajireddy15/student_grade_pred
A machine learning project to predict student final grades using academic and demographic data. Built with pandas, scikit-learn, and visualized with seaborn and matplotlib to gain insights and support early intervention for students.
https://github.com/rajireddy15/student_grade_pred
academic-insights data-science eda education-analytics grade-prediction machine-learning ml-project pandas regression-models scikit-learn student-performance-analysis
Last synced: about 1 month ago
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A machine learning project to predict student final grades using academic and demographic data. Built with pandas, scikit-learn, and visualized with seaborn and matplotlib to gain insights and support early intervention for students.
- Host: GitHub
- URL: https://github.com/rajireddy15/student_grade_pred
- Owner: RajiReddy15
- Created: 2025-06-16T05:21:46.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-16T05:24:11.000Z (12 months ago)
- Last Synced: 2025-06-16T06:23:49.059Z (12 months ago)
- Topics: academic-insights, data-science, eda, education-analytics, grade-prediction, machine-learning, ml-project, pandas, regression-models, scikit-learn, student-performance-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 228 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎓 Student Grade Prediction
This project aims to predict student final grades based on various academic and personal attributes using machine learning models.
## 📌 Project Overview
The objective is to build a predictive model that can estimate students' final grades using features such as past academic performance, study time, absences, and other factors. This can help educators identify students who may need extra support.
## 📂 Dataset
The dataset contains features such as:
- First and second period grades
- Study time, absences
- Parental education
- Past failures
- Other demographic and academic attributes
Target variable: **Final Grade**
## 🛠️ Tech Stack
- **Python**
- **pandas** – Data loading & manipulation
- **numpy** – Numerical computations
- **matplotlib & seaborn** – Data visualization
- **scikit-learn** – Model building and evaluation
## 📊 Exploratory Data Analysis (EDA)
- Handled missing values and removed irrelevant features
- Visualized correlations using heatmaps and pairplots
- Analyzed the impact of key factors (e.g., study time, absences) on final grades
## 🤖 Models Used
- Linear Regression
- Random Forest Regressor
### 📈 Model Evaluation
- Used metrics like MAE, RMSE, and R² Score
- Compared performance between models on test data
## 📌 Results
- Random Forest outperformed other models with higher accuracy on unseen data
- Identified key factors influencing academic performance
## 🚀 How to Run
1. Clone this repository
2. Install dependencies from `requirements.txt`
3. Run the Jupyter Notebook:
```bash
jupyter notebook main.ipynb
```
## 📁 Project Structure
```
student_grade_prediction/
├── main.ipynb
├── requirements.txt
├── README.md
├── datasets/
│ ├── student-mat.csv
│ └── student-por.csv
```
## 📬 Contact
For questions or collaboration, feel free to connect on [LinkedIn](https://www.linkedin.com/in/raji-reddy-bb54682aa/).