Predict student math performance using advanced ML algorithms trained on comprehensive educational data
🚀 Start Predicting NowThis project began with analyzing a comprehensive dataset of 1000+ student records. Through EDA, I explored data distributions, identified patterns, detected outliers, and understood correlations between different variables to inform model development.
I implemented robust preprocessing pipelines using scikit-learn's ColumnTransformer to handle both numerical and categorical features separately. This included standardization for numerical features and encoding for categorical variables (gender, race, education level, lunch type, test preparation status).
I trained and experimented with various machine learning algorithms including Linear Regression, Ridge Regression, Lasso Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines to find the best performing model for this prediction task.
I compared all models using performance metrics like R² Score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Through careful analysis and comparison of these metrics, I selected the best-performing model that provides accurate and reliable predictions.
To optimize model performance, I conducted hyperparameter tuning using techniques like Grid Search and Randomized Search. This involved testing different parameter combinations to find the optimal configuration that maximizes the model's predictive accuracy.
The trained model and preprocessing pipeline were serialized into pickle files for easy storage and deployment. This allows the model to be loaded and used in production without retraining, enabling real-time predictions through this web interface.
This project represents my comprehensive learning of Machine Learning concepts and practices. It covers the entire ML pipeline from data exploration to model deployment. This is my first machine learning project, and I'm excited to showcase what I've learned! Many more projects are on the way as I continue my journey in data science and machine learning. 🚀