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Student Well Beings

by norochalise Updated: Nov 2, 2021

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.

jupyterby jupyter

Python star image 12379 Version:Current

License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat

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jupyterby jupyter

Python star image 12379 Version:Current License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat
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Data Mining

We used pandas and numpy for Data Mining.

pandasby pandas-dev

Python star image 33259 Version:v1.4.1

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 33259 Version:v1.4.1 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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numpyby numpy

Python star image 20101 Version:v1.22.3

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python star image 20101 Version:v1.22.3 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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Data Visualizations

We used matplotlib and seaborn python libraries for data visualizations.

matplotlibby matplotlib

Python star image 15355 Version:v3.5.1

License: No License (null)

matplotlib: plotting with Python

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matplotlibby matplotlib

Python star image 15355 Version:v3.5.1 License: No License

matplotlib: plotting with Python
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seabornby mwaskom

Python star image 9320 Version:v0.11.2

License: Permissive (BSD-3-Clause)

Statistical data visualization in Python

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seabornby mwaskom

Python star image 9320 Version:v0.11.2 License: Permissive (BSD-3-Clause)

Statistical data visualization in Python
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Machine learning

We used Sklearn python libraries for binary classification problem solve.

scikit-learnby scikit-learn

Python star image 49728 Version:1.0.2

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python star image 49728 Version:1.0.2 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python
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Kit Solution Source

Student-well-beingsby norochalise

Jupyter Notebook star image 0 Version:Current

License: Permissive (MIT)

Based on the students information we classify mental disorder and parent satisfactions. Both binary classifications problem we solved by traditional machine learning. algorithms.

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Student-well-beingsby norochalise

Jupyter Notebook star image 0 Version:Current License: Permissive (MIT)

Based on the students information we classify mental disorder and parent satisfactions. Both binary classifications problem we solved by traditional machine learning. algorithms.
Support
Quality
Security
License
Reuse

Deployment Information

Our solution and all dependent assets are available in the below repository.

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.

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