School Achievment Tool

You can check out the full version here https://ml-school-tool.vercel.app/

Currently an ongoing project of the Dept. of EFLT at Auburn University, this is a Flask and React based machine learning tool to improve Alabama school performance. The models used are run on a static dataset hosted on the flask server. The tool works by first utilizing Lasso Regression to reduce the total amount of features present in the dataset. Then a reverse prediction is made utilizing ExtraTreesRegressor. The user can change the parameters of the models used in each step. For each trial, the user is presented with 2 main pieces of data

1. Lasso provides feature strengths from the whole dataset - this gives a general idea of what broadly impacts performance, positively or negatively
2. Reverse prediction provides the percent change for each feature needed to attain a certain increase in a schools performance

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How to Build It Yourself (Current Version)

Flask/backend here and React/frontend here.

1. Clone the Flask Repository
   $ git clone https://github.com/bls0053/ml-school-flask.git

2. Activate Python Virtual Environment
   $venv/scripts/activate

3. Install Python Dependencies
   $ pip install -r requirements.txt

4. Run the Flask Server
   $ Python main.py

5. Clone the React Repository
   $ git clone https://github.com/bls0053/ml-school-tool.git

6. Install React Dependencies
   $ npm install

7. Run the Development Server
   $ npm run dev

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How to Build It Yourself (Single Repo - outdated version)

Note: Monorepo build only available on the 'Final version' commit here and all previous commits. Later commits split the repo for deployment purposes - Flask/backend here and React/frontend here.

1. Clone the Repository
   $git clone https://github.com/bls0053/ml-school-tool.git
   Checkout local version:
   $git checkout 16cca6d

2. Activate Python Virtual Environment
   Navigate into the api directory:
   $ cd ml-school-tool/api
   Run:
   $venv/scripts/activate

3. Install Python Dependencies
   Navigate into venv:
   $ cd ml-school-tool/api/venv
   Run:
   $ pip install -r requirements.txt

4. Run the Flask Server
   $ Flask run

5. Install React Dependencies
   Navigate into project folder:
   $ cd ml-school-tool
   Run:
   $ npm install

6. Run the Development Server
   Start the development server with:
   $ npm run dev

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