Student well beings help us to predict mental disorder and parent satisfaction based on the the students information. Both are binary classification problem and we used to solved by traditional machine learning algorithms. This is the project from Team Neuron Fire.

Development Environment

We used Jupyter Notebooks by developing and debugging. Jupyter Notebook is a web based interactive environment often used for experiments.

Data Mining

We used pandas and numpy for Data Mining.

Kit Deployment Instructions

The entire solution is available as a package to download and install from the source code repository. Prerequisite: Python3 Follow below instructions to download and deploy the solution. 1. Open Command Line Interface 2. Run the command 'git clone https://github.com/norochalise/Student-well-beings.git' to clone the repository 3. Run the command 'cd Student-well-beings' to change directory 4. Run the command 'pip install -r requirements.txt' to install all the dependencies 5. Run the command 'jupyter notebook' to open the Jupyter Notebook 6. Click 'mental_disorder-classification.ipynb' to open the notebook 7. Execute all the cells in the notebook 8. Same way open parent_satisfaction_classification.ipynb and Execute all the cells in the notebook If there're any challenges while installing dependencies, run the command below to upgrade pip and try again. python -m pip install --upgrade pip Some command can be different based on the OS so please consider it.