Predicting Students Grades Using Machine Learning

Overview

This project explores the use of machine learning models to predict students' grades based on various academic, demographic, and behavioral factors. It applies a robust data preprocessing pipeline and compares the performance of multiple machine learning algorithms to identify the best predictive model.

The project is a demonstration of my ability to handle real-world data, build predictive models, and perform comprehensive analysis to derive actionable insights.

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Features

• Data Preprocessing:

  • Handled missing values, normalization, and feature engineering.
  • Encoded categorical variables to ensure compatibility with machine learning models.

• Machine Learning Models:

  • Compared various algorithms, including:
    • Linear Regression
    • Decision Trees
    • Random Forests
    • Support Vector Machines (SVM)
    • Gradient Boosted Trees
  • Tuned hyperparameters using techniques like Grid Search and Cross-Validation.

• Evaluation Metrics:

  • Evaluated models using RMSE, R-squared, and other relevant performance metrics.
  • Identified the most influential features contributing to students' performance.

Predicting Students' Grades Using Machine Learning

Overview

This project explores the use of machine learning models to predict students' grades based on various academic, demographic, and behavioral factors. It applies a robust data preprocessing pipeline and compares the performance of multiple machine learning algorithms to identify the best predictive model.

The project is a demonstration of my ability to handle real-world data, build predictive models, and perform comprehensive analysis to derive actionable insights.

---

Features

• Data Preprocessing:

  • Handled missing values, normalization, and feature engineering.
  • Encoded categorical variables to ensure compatibility with machine learning models.

• Machine Learning Models:

  • Compared various algorithms, including:
    • Linear Regression
    • Decision Trees
    • Random Forests
    • Support Vector Machines (SVM)
    • Gradient Boosted Trees
  • Tuned hyperparameters using techniques like Grid Search and Cross-Validation.

• Evaluation Metrics:

  • Evaluated models using RMSE, R-squared, and other relevant performance metrics.
  • Identified the most influential features contributing to students' performance.

Contact

For questions or collaboration opportunities, feel free to reach out.