https://github.com/alfastrek/future-scholar
Data Science Project with live dashboard, analytics and showcasing predictive analytics on student exam performance.
https://github.com/alfastrek/future-scholar
analytics azure cloud dashboard data-science flask machine-learning mlops python visualization
Last synced: 3 months ago
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Data Science Project with live dashboard, analytics and showcasing predictive analytics on student exam performance.
- Host: GitHub
- URL: https://github.com/alfastrek/future-scholar
- Owner: Alfastrek
- Created: 2024-06-29T21:37:24.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-15T19:40:11.000Z (over 1 year ago)
- Last Synced: 2025-03-16T18:46:27.235Z (over 1 year ago)
- Topics: analytics, azure, cloud, dashboard, data-science, flask, machine-learning, mlops, python, visualization
- Language: Jupyter Notebook
- Homepage: https://scorepredictor.aradhya.site
- Size: 1.37 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Student Score Predictor - Aradhya Shukla 🧑🏻🎓🌟
### Introduction About the Data :
#### Dataset: Students Performance in Exams - https://www.kaggle.com/code/aryanml007/students-performance-analysis/input
## Description
This dataset contains information about student performance on exams, including:
- `gender`: Gender of the student (male/female)
- `ethnicity`: Ethnicity of the student
- `parental_level_of_education`: Highest education level of the student's parents
- `lunch`: Type of lunch the student has (standard or free/reduced)
- `test_preparation_course`: Whether the student completed a test preparation course (completed or none)
- `math_score`: Score obtained in the math exam
- `reading_score`: Score obtained in the reading exam
- `writing_score`: Score obtained in the writing exam
**Size:** The dataset consists of approximately 1000 rows (students) and 8 columns (features).
## Approach for the project
1. Data Ingestion :
* In Data Ingestion phase the data is first read as csv.
* Then the data is split into training and testing and saved as csv file.
2. Data Transformation :
* In this phase a ColumnTransformer Pipeline is created.
* for Numeric Variables first SimpleImputer is applied with strategy median , then Standard Scaling is performed on numeric data.
* for Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler.
* This preprocessor is saved as pickle file.
3. Model Training :
* In this phase base model is tested . The best model found was catboost regressor.
* After this hyperparameter tuning is performed on catboost and knn model.
* A final VotingRegressor is created which will combine prediction of catboost, xgboost and knn models.
* This model is saved as pickle file.
4. Prediction Pipeline :
* This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python.
5. Flask App creation :
* Flask app is created with User Interface to predict the gemst
## Possible Uses
- Analyze factors influencing student performance
- Identify areas for improvement in educational programs
- Predict student performance based on various factors
- Gender Performance: Compare performance between male and female students.
- Ethnicity Impact: Assess how ethnicity affects academic results.
- Parental Education: Explore the influence of parents' education levels on student scores.
- Lunch Type: Determine if lunch standard impacts student performance.
- Test Preparation: Evaluate the effect of test prep courses on exam scores.
## Target Variable
- `math_score`: Score obtained in the math exam
# AZURE Deployment Link :
(https://scorepredictor.aradhya.site/)
# Screenshot of UI
one prices inside a Web Application.