https://github.com/naveen88112/final_education

Student Performance Prediction This project examines the student performance data, pre-processes the features, and implements machine learning methods (Random Forest) for the forecasting of final grades. The data is subjected to exploratory data analysis (EDA) and feature engineering prior to model training and assessment.
https://github.com/naveen88112/final_education

data-preprocessing exploratory-data-analysis machine-learning model-evaluation numpy pandas python

Last synced: 3 months ago
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Student Performance Prediction This project examines the student performance data, pre-processes the features, and implements machine learning methods (Random Forest) for the forecasting of final grades. The data is subjected to exploratory data analysis (EDA) and feature engineering prior to model training and assessment.

• Host: GitHub
• URL: https://github.com/naveen88112/final_education
• Owner: Naveen88112
• Created: 2025-03-11T08:11:22.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-11T09:17:50.000Z (3 months ago)
• Last Synced: 2025-03-11T10:30:22.051Z (3 months ago)
• Topics: data-preprocessing, exploratory-data-analysis, machine-learning, model-evaluation, numpy, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 21.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Student Performance Prediction

Overview

This project focuses on predicting student performance using machine learning techniques. It involves preprocessing educational data, conducting exploratory data analysis (EDA), and training a classification model to predict final grades.

Features

- Data Preprocessing: Standardization, encoding of categorical features, and handling missing values.

- Exploratory Data Analysis(EDA): Statistical summaries and visualizations.

- Machine Learning Model: Random Forest Classifier trained to predict student grades.

- Performance Evaluation: Accuracy score used to assess model effectiveness.

Technologies Used

- Python

- Pandas & NumPy

- Scikit-learn

- Matplotlib & Seaborn

How to Run

1. Clone the repository:

   

   "git clone https://github.com/yourusername/student-performance-prediction.git"

2. Open the Jupyter Notebook or Google Colab.

3. Upload the dataset (if required) and execute the cells step by step.

Results & Insights

- Feature preprocessing improved model performance.

- The Random Forest model was used to classify student performance.

- EDA provided insights into the factors affecting student grades.