https://github.com/farhad-here/predict_student_performance
Predict Student Performance, is a data analysis and machine learning project aimed at predicting students' final performance (g3) based on demographic, family, and academic features. The project supports both Regression (predicting exact grades) and classification (Pass/Fail categories).
https://github.com/farhad-here/predict_student_performance
classification data-analysis data-visualization linear-regression machine-learning numpy pandas postgresql powerbi scikit-learn streamlit
Last synced: 2 months ago
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Predict Student Performance, is a data analysis and machine learning project aimed at predicting students' final performance (g3) based on demographic, family, and academic features. The project supports both Regression (predicting exact grades) and classification (Pass/Fail categories).
- Host: GitHub
- URL: https://github.com/farhad-here/predict_student_performance
- Owner: farhad-here
- License: mit
- Created: 2025-08-21T12:48:33.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-21T13:06:51.000Z (10 months ago)
- Last Synced: 2025-09-11T14:55:09.307Z (9 months ago)
- Topics: classification, data-analysis, data-visualization, linear-regression, machine-learning, numpy, pandas, postgresql, powerbi, scikit-learn, streamlit
- Language: Jupyter Notebook
- Homepage: https://predictstudentperformance.streamlit.app/
- Size: 4.15 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🎓 Student Performance Prediction: An End-to-End Data analysis Project 🚀
---
## ✨ Project Summary
This project demonstrates a complete and practical approach to data science, with the goal of **predicting student academic performance** based on various social, family, and educational factors. Through this project, a full data pipeline has been created, covering everything from exploratory data analysis (EDA) to the construction and evaluation of a machine learning model.
This project can help educators and institutions proactively identify students at risk of underperformance, allowing for timely and effective interventions.
---
## 📓 Description
An end-to-end data analysis and machine learning project for predicting student performance using educational and social data. The project includes data analysis, model building, and an interactive dashboard.
---
## 🎯 The Problem
Student academic success is influenced by numerous factors. By analyzing data related to study habits, family status, and other behavioral characteristics, we can build a model that predicts which students might receive a low final grade. This prediction enables a proactive, preventative approach rather than a reactive one.
---
## 🛠️ Project Architecture & Workflow
This project follows a structured and modular workflow:
1. **Data Collection & Cleaning:**
* The dataset is loaded from a CSV file, and cleaning and preprocessing operations (such as handling missing values and correcting data types) are performed.
2. **Exploratory Data Analysis (EDA):**
* A comprehensive statistical and visual analysis is conducted to uncover hidden relationships between features and the final student grade.
* Visualizations such as a Correlation Matrix, Histograms, and Scatter Plots are created for a better understanding of the data.
3. **Feature Engineering & Preprocessing:**
* Categorical features are encoded numerically to make them suitable for machine learning models.
* The data is split into training and testing sets.
4. **Machine Learning Model:**
* A classification model is trained to predict the final student grade (e.g., pass or fail).
* The model's performance is measured using appropriate evaluation metrics (such as accuracy, F1-Score, and a Confusion Matrix).
5. **Conclusion & Insights:**
* A summary of key findings and important insights derived from the data analysis is presented.
---
## 💻 Technical Stack & Libraries
* **Programming Language:** Python
* **Data Analysis:** `Pandas`, `NumPy`
* **Data Visualization:** `Matplotlib`, `Seaborn`
* **Machine Learning:** `Scikit-learn`
* **Development Environment:** `Jupyter Notebook`
---
## 🏙️ Dataset
The dataset includes features like:
- **Demographic**: `sex`, `age`, `address`, `famsize`
- **Family and Education**: `medu`, `fedu`, `mjob`, `fjob`
- **Lifestyle and Social**: `famrel`, `freetime`, `goout`, `dalc`, `walc`, `health`
- **Academic**: `absences`, `g1`, `g2`, and the target `g3`
---
## ✋ Approach
- **Regression**
- Predict `g3` as a numerical value (range: 0–20).
- **Classification**
- Convert `g3` into two classes:
- **Fail**: 0–9
- **Pass**: 10–20
---
## ▶️ How to Run the Project
Follow these steps to run the project on your local machine:
1. **Clone the Repository:**
```bash
git clone [https://github.com/farhad-here/Predict_student_performance.git](https://github.com/farhad-here/Predict_student_performance.git)
cd Predict_student_performance
```
2. **Install Dependencies:**
* It is highly recommended to use a virtual environment.
```bash
pip install -r requirements.txt
```
3. **Run the Project:**
* Open the `Jupyter Notebook` file and run all the cells in order.
```bash
jupyter notebook
```
## ✉️ PowerBi Dashboard
