Student performance prediction using MLops

Demo video of Student performance prediction

Student.mp4

Welcome to the Student Performance Prediction using MLops in Regression project! In this repository, we present an end-to-end solution for predicting student performance using Machine Learning and adopting MLOps practices. This README provides an overview of the model, its motivation, success metrics, requirements, methodology, architecture, pipeline, and conclusion.

1. Overview of the Model

This project understands how the student’s performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, and Lunch and Test preparation course.

2. Motivation of the Model

The motivation behind this model is to assist educational institutions in identifying students who might be at risk of underperforming. By predicting student performance early, educators can provide timely interventions and support, potentially improving overall success rates.

3. Success Metrics

The success of the model will be evaluated based on metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared score, which measure the accuracy and goodness-of-fit of the regression model.

4. Requirements & Constraints

4.1. Functional Requirements

Access to labeled student performance dataset. Machine Learning libraries (e.g., scikit-learn, TensorFlow). Deployment platform (e.g., Docker, cloud services).

4.2. Non-Functional Requirements

Scalability: The model should be able to handle a growing number of students and features.

Latency: Real-time predictions for timely interventions.

Security: Protecting student data and ensuring compliance with data privacy regulations.

4.3. Constraints

Availability and quality of input data may impact the model’s accuracy. Resource constraints might affect the choice of algorithms and deployment options.

4.4. Out-of-Scope

Individual student-level interventions are out of scope; the model provides predictions but not recommendations for action.

5. Methodology

5.1. Problem Statement

Given historical student performance data and relevant features, build a regression model that can accurately predict the final grades of students.

5.2. Data

The dataset should include features like study hours, attendance, previous exam scores, and final grades. It should be split into training and testing sets for model evaluation.

5.3. Techniques

Data preprocessing: Handle missing values, feature scaling, and encoding categorical variables.

Feature selection: Identify the most relevant features for prediction.

Model selection: Choose regression algorithms (e.g., Linear Regression, Random Forest) and perform hyperparameter tuning.

MLOps integration: Implement CI/CD, model monitoring, and automated retraining for continuous improvement.

6. Architecture

The architecture consists of the following components:

1. Data Ingestion: Fetch and preprocess data.
2. Data preprocessing: Implement the data preprocessing technique.
3. Model Training: Build and train the regression model.
4. Model Evaluation: Assess model performance using test data.
5. Model Deployment: Deploy the model using a suitable platform.
6. MLOps Pipeline: Automate data updates, model retraining, and deployment.

7. Pipeline

1. Data Collection & Preprocessing
2. Feature Engineering & Selection
3. Model Building & Training
4. Model Evaluation & Metrics Calculation
5. Model Deployment & Monitoring
6. Continuous Integration & Continuous Deployment (CI/CD)
7. Model Retraining based on Data Drift

8. Conclusion

In this project, we have showcased a comprehensive approach to predict student performance using a regression model and MLOps practices. By accurately predicting student grades, educational institutions can take proactive measures to support struggling students and enhance overall academic success.

Life cycle of this project:

If you can read on more about my project in detail please click my blog: https://www.analyticsvidhya.com/blog/2023/04/student-performance-analysis-and-prediction/