{"id":20754053,"url":"https://github.com/chandima2000/students-performance-predictor","last_synced_at":"2026-04-07T08:01:52.686Z","repository":{"id":244693893,"uuid":"815978560","full_name":"chandima2000/students-performance-predictor","owner":"chandima2000","description":"This is an End to End Production Grade Data Science Project.","archived":false,"fork":false,"pushed_at":"2024-06-26T20:32:29.000Z","size":1872,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-11T15:27:13.068Z","etag":null,"topics":["azure","ci-cd","data-science","flask","mlops","python"],"latest_commit_sha":null,"homepage":"https://student-performance-indicator.azurewebsites.net/","language":"Jupyter 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the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course. \n- Based on these Gender, Ethnicity, Parental level of education, Lunch and Test preparation courses, we will predict the students' performance.\n\n## Project Description\n\n- The project covers `Data preprocessing, Model training, Hyperparameter Tunning and Evaluation`.\n- Main focus areas: `Modular Coding, MLops, Cloud Services, and CI/CD`.\n- Utilizes Custom Exception handling mechanisms and custom logs to record each action.\n- Implemented using Industry Standard Folder Structure.\n- Research conducted using Jupyter Notebook and stored in the Notebook folder.\n- Cloud Deployment: The project is deployed on `Microsoft Azure` for scalability and accessibility.\n- CI/CD: Implemented using `GitHub Actions` to automate integration \u0026 deployment.\n\n### Backend\n- Flask web Framework\n### Frontend\n- HTML \u0026 CSS\n\n### Dataset\n\n- Source: - https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977\n\n- Description: The dataset contains 1000 instances with 8 parameters.\n\n- Preprocessing: Missing values were imputed, and categorical features were encoded using `One Hot Encoding`. And also use `StandardScaler` for standardization.\n\n### Methodology\n\n- Data Cleaning: Handled missing values and outliers.\n- Exploratory Data Analysis: Visualized distributions and correlations. The `EDA STUDENT PERFORMANCE.ipynb` file shows the visualization part.\n- Feature Engineering: Created new features based on existing data.\n- Modeling: Trained `Decision Tree, Random Forest, Gradient Boosting, Linear Regression, XGBRegressor, AdaBoost Regressor` models. `Hyperparameter Tunning` is done for all models.\n- Evaluation: Used accuracy, precision and R2-score to evaluate models.\n\n## Installation\nFollow these steps to install the necessary dependencies and set up the project.\n\n- clone the repository:\n````bash \n    git clone https://github.com/chandima2000/students-performance-predictor.git\n````\n- go to the root folder: \n````bash\n    cd students-performance-predictor\n````\n- build project: \n````bash \n    pip install -r requirements.txt\n````\n## Usage\nTo use the Student Performance Predictor website, run the following command:\n\n````bash \n    python app.py\n````\n## You can Try 👇\n- URL: https://student-performance-indicator.azurewebsites.net/\n  \n## User Interface\n![image](https://github.com/chandima2000/students-performance-predictor/assets/101726882/4118a56f-0b1f-4a17-9e88-0a92aa6b438d)\n\n## Contributions\nAll contributions are welcome. Feel free to open issues or submit pull requests.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandima2000%2Fstudents-performance-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchandima2000%2Fstudents-performance-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandima2000%2Fstudents-performance-predictor/lists"}