Solar Panel Efficiency Management Model Kit
by sarthakkiran Updated: Jan 9, 2022
Team Name: VIT/OW/55 "The future is green energy, sustainability, renewable energy. Once you got a solar panel on a roof, energy is free. Once we convert our entire electricity grid to green and renewable energy, the cost of living goes down." Welcome to our Sustainable Energy Management Hackathon kit. Check out our video Report!! https://drive.google.com/file/d/1me1sje1o5Lrm8SkKQpQy9Sx5j_2Itowi/view?usp=sharing
Exploratory Data Analysis in Python
These libraries are used for statistical, Univariate, descriptive, and exploratory analysis.
matplotlib: plotting with Python
Python 17088 Version:v3.7.1 License: No License
Statistical data visualization in Python
Python 10513 Version:v0.12.2 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
Python 37428 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)
The fundamental package for scientific computing with Python.
Python 23036 Version:v1.24.2 License: Permissive (BSD-3-Clause)
Machine Learning Libraries for Prescriptive Analysis
These libraries are used for developing the machine learning pipeline model .
scikit-learn: machine learning in Python
Python 53544 Version:1.2.2 License: Permissive (BSD-3-Clause)
An open-source, low-code machine learning library in Python
Jupyter Notebook 7086 Version:3.0.0 License: Permissive (MIT)
Automated Machine Learning with scikit-learn
Python 6802 Version:v0.15.0 License: Permissive (BSD-3-Clause)
Annotation & Labelling libraries
These are used for data visualization, data Labelling, data annotation.
A PyPI package for easy text annotation in a Jupyter Notebook.
Python 11 Version:v1.0.2 License: Strong Copyleft (GPL-3.0)
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Python 12415 Version:1.7.2 License: Permissive (Apache-2.0)
Labelbox is the fastest way to annotate data to build and ship computer vision applications.
This section has the instructions to install the kit. For theSarthak & Ashutosh kit source, the deployment instructions are: 1. Clone our github repository from the source: https://github.com/Sarthak1807/Sustainable-Energy-Hackathon 2. Download and save all your files. 3. For the Prescriptive and Exploratory data analysis, Navigate to the 'energy hack.ipynb' and open and run each cell. 4. For the Statistical and Descriptive, time Series analysis , Navigate to the 'energy hack.ipynb' and open and run each cell. 5. For the Data Labelling and Annotation, Navigate to the 'tortus_labelling.ipynb' and open and run each cell. 6. Install the required libraries by 'pip install -r requirements.txt' 7. Read out report.