Team 7 - Student Performance Analysis Kit
by hemanthnag132 Updated: Jan 27, 2022
Solution Kit
This is a basic student performance analysis with predefined data using basics of machine learning. We used a predetermined dataset consisting of students marksheets. The output analysis is visualized using graphs for each subject and its chapters. It is done using K-Clustering algorithom which is an unsupervised learning algorithm. Here a subject mark's labels are grouped into one single cluster. This helps to analyse the student's performance and help him do better in upcoming exams
Libraries Used
We have used the below libraries to implement data analysis and to visualise student's data
numpyby numpy
The fundamental package for scientific computing with Python.
numpyby numpy
Python
23587
Version:v1.24.3
License: Permissive (BSD-3-Clause)
streamlitby streamlit
Streamlit — A faster way to build and share data apps.
streamlitby streamlit
Python
24943
Version:1.22.0
License: Permissive (Apache-2.0)
pandasby pandas-dev
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python
38499
Version:v2.0.2
License: Permissive (BSD-3-Clause)
matplotlibby matplotlib
matplotlib: plotting with Python
matplotlibby matplotlib
Python
17450
Version:v3.7.1
License: No License
dashby plotly
Data Apps & Dashboards for Python. No JavaScript Required.
dashby plotly
Python
18757
Version:v2.10.0
License: Permissive (MIT)
ipythonby ipython
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
ipythonby ipython
Python
15827
Version:7.18.1
License: Permissive (BSD-3-Clause)
Kit Solution Source
Techforgood2022by muditbaid
Techforgood2022by muditbaid
Python
0
Version:Current
License: No License
Deployment Information
Deployed Application: https://share.streamlit.io/muditbaid/techforgood2022/main/modplott.py