Student Scores
PREDICTING STUDENTS' AVERAGE PERFORMANCE
There are numerous influences that can affect how a student will perform on exams, including physical, social, and environmental. The impact could be negative, positive, or nothing. This analysis seeks to show how data could be used to understand these influences.
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Table of Contents
About The Project
Built With
- Powerpoint
- Word
- jupyter notebook
Getting Started
To get a local copy up and running follow these simple steps.
Prerequisites
- Microsoft Office Suite
- PDF Reader
Installation
- Clone the repo
git clone https://github.com/AMeyer89/StudentScores.git
Description
Standardized testing has been included in the educational system throughout history and has caused controversy. This type of testing measures a student’s progress at any given time in their education. There are socioeconomic aspects that could impact a student test scores. The purpose of this project is to show how using data could prove or disprove the impact of these socioeconomic influences and thus proving standardized testing is not biased or unbiased. The impact could be negative, positive, or nothing. The methods used was Exploratory Data Analysis in Python. Linear regression and random forest regressor models were used from Supervised machine learning models in Sklearn.
Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE for more information.
Contact
April Meyer - swim53185@gmail.com
Project Link: https://github.com/AMeyer89/StudentScores