Maths Score Predictor is a machine learning project that aims to predict math scores based on various features using a Kaggle dataset. This repository contains all the necessary files and code to take you through the entire process, from data collection and preprocessing to model training and deployment.
Maths Score Predictor is a comprehensive project that demonstrates the end-to-end process of developing a machine learning model for predicting math scores. The project includes data collection, data preprocessing, model selection, training, evaluation, and deployment. Whether you're new to machine learning or looking to expand your skills, this repository can serve as a valuable resource.
The dataset used for this project is sourced from kaggle : https://www.kaggle.com/datasets/spscientist/students-performance-in-exams
It contains information about various factors that may influence math scores. The dataset includes features like student demographics, study time, parental education level, and more.
Clone the repository:
git clone https://github.com/your-username/Maths-Score-Predictor.git
cd Maths-Score-PredictorInstall the required dependencies:
python setup.py Run the application :
streamlit run app.pyThis project is organized into different stages, each implemented in a separate Jupyter Notebook.
In src/components/
There are python files such as data_ingestion.py , data_transformation.py .In This files the complete data preprocessing steps are taken care of from data cleaning to data transformation.
In src/components/
There is a python file named model_trainer.py , It involves the model training part. Various algorithms are explored, and their performance is evaluated using appropriate metrics.
The src/utils.py notebook focuses on evaluating the trained models in detail. It includes analysis of the model's performance on the test dataset.
The trained model is deployed using streamlit.
You can run :
streamlit run app.pyor
You can go to : https://mathscorepredictor.streamlit.app/ to use the deployed application.
Contributions to this repository are welcome. If you find any issues or want to add new features, please feel free to submit a pull request.
For detailed information, refer to the individual notebooks and files within the repository. If you have any questions or feedback, feel free to contact me at [email protected] Happy predicting!