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https://github.com/chandima2000/students-performance-predictor
This is an End to End Production Grade Data Science Project.
https://github.com/chandima2000/students-performance-predictor
azure ci-cd data-science flask mlops python
Last synced: 2 days ago
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This is an End to End Production Grade Data Science Project.
- Host: GitHub
- URL: https://github.com/chandima2000/students-performance-predictor
- Owner: chandima2000
- Created: 2024-06-16T17:57:10.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-06-26T20:32:29.000Z (5 months ago)
- Last Synced: 2024-06-27T19:30:11.236Z (5 months ago)
- Topics: azure, ci-cd, data-science, flask, mlops, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.79 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Student Performance Predictor
## Introduction
- This is an `End to End Production Grade Data Science Project`.
- This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course.
- Based on these Gender, Ethnicity, Parental level of education, Lunch and Test preparation courses, we will predict the students' performance.
## Project Description
- The project covers `Data preprocessing, Model training, Hyperparameter Tunning and Evaluation`.
- Main focus areas: `Modular Coding, MLops, Cloud Services, and CI/CD`.
- Utilizes Custom Exception handling mechanisms and custom logs to record each action.
- Implemented using Industry Standard Folder Structure.
- Research conducted using Jupyter Notebook and stored in the Notebook folder.
- Cloud Deployment: The project is deployed on `Microsoft Azure` for scalability and accessibility.
- CI/CD: Implemented using `GitHub Actions` to automate integration & deployment.
### Backend
- Flask web Framework
### Frontend
- HTML & CSS
### Dataset
- Source: - https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977
- Description: The dataset contains 1000 instances with 8 parameters.
- Preprocessing: Missing values were imputed, and categorical features were encoded using `One Hot Encoding`. And also use `StandardScaler` for standardization.
### Methodology
- Data Cleaning: Handled missing values and outliers.
- Exploratory Data Analysis: Visualized distributions and correlations. The `EDA STUDENT PERFORMANCE.ipynb` file shows the visualization part.
- Feature Engineering: Created new features based on existing data.
- Modeling: Trained `Decision Tree, Random Forest, Gradient Boosting, Linear Regression, XGBRegressor, AdaBoost Regressor` models. `Hyperparameter Tunning` is done for all models.
- Evaluation: Used accuracy, precision and R2-score to evaluate models.
## Installation
Follow these steps to install the necessary dependencies and set up the project.
- clone the repository:
````bash
git clone https://github.com/chandima2000/students-performance-predictor.git
````
- go to the root folder:
````bash
cd students-performance-predictor
````
- build project:
````bash
pip install -r requirements.txt
````
## Usage
To use the Student Performance Predictor website, run the following command:
````bash
python app.py
````
## You can Try 👇
- URL: https://student-performance-indicator.azurewebsites.net/
## User Interface
## Contributions
All contributions are welcome. Feel free to open issues or submit pull requests.