https://github.com/saritaphd/predicting-performance-of-students---complete-ml-project-with-deployment-using-aws

Student performance analysis with deployment (End to end ML project)
https://github.com/saritaphd/predicting-performance-of-students---complete-ml-project-with-deployment-using-aws

aws data data-science deployment jupyter-notebook machine-learning python visualization

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Student performance analysis with deployment (End to end ML project)

• Host: GitHub
• URL: https://github.com/saritaphd/predicting-performance-of-students---complete-ml-project-with-deployment-using-aws
• Owner: SaritaPhD
• Created: 2023-06-26T11:36:53.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-30T13:45:52.000Z (about 1 year ago)
• Last Synced: 2023-10-30T14:39:51.298Z (about 1 year ago)
• Topics: aws, data, data-science, deployment, jupyter-notebook, machine-learning, python, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 370 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## End to End Machine Learning Project (Student Performance Indicator) with deployment using AWS

- This project aims to understand how student performance, measured by test scores, is affected by various variables such as gender, ethnicity, parental level of education, lunch, and test preparation course.  

- Project Overview

The project follows the standard life cycle of a machine learning project, including data collection, data preprocessing, exploratory data analysis, model training, and model selection.

- Dataset

The dataset used for this project is taken from kaggle given in EDA file. It consists of 1000 records with 8 columns:

- gender: Sex of the student (Male/Female)

- race/ethnicity: Ethnicity of the student (Group A, B, C, D, E)

- parental level of education: Parent's final education (Bachelor's degree, Some college, Master's degree, Associate's degree, High school)

- lunch: Whether the student had lunch before the test (Standard or Free/Reduced)

- test preparation course: Whether the student completed the test preparation course before the test (Complete or Not Complete)

- math score: Student's score in math

- reading score: Student's score in reading

- writing score: Student's score in writing

- Project Workflow

- Understanding the Problem Statement (Business problem and data understanding)

- Data Collection

- Data Cleaning and Preprocessing

- Exploratory Data Analysis

- Model Training

- Model Selection

- Model Deployment

### Conclusion

Through this project, we gain insights into how student performance is influenced by different factors. By understanding these relationships, we can identify key factors that contribute to improved student performance and make informed decisions to support student learning and success.