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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.
pandasby pandas-dev
Python 33259 Version:v1.4.1
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Data Visualizations
We used matplotlib and seaborn python libraries for data visualizations.
matplotlibby matplotlib
Python 15355 Version:v3.5.1
matplotlib: plotting with Python
matplotlibby matplotlib
Python 15355 Version:v3.5.1 License: No License
Machine learning
We used Sklearn python libraries for binary classification problem solve.
scikit-learnby scikit-learn
Python 49728 Version:1.0.2
scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 49728 Version:1.0.2 License: Permissive (BSD-3-Clause)
Kit Solution Source
Student-well-beingsby norochalise
Jupyter Notebook 0 Version:Current
Based on the students information we classify mental disorder and parent satisfactions. Both binary classifications problem we solved by traditional machine learning. algorithms.
Student-well-beingsby norochalise
Jupyter Notebook 0 Version:Current License: Permissive (MIT)
Deployment Information
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.